# Context-aware Adversarial Training for Name Regularity Bias in
Named Entity Recognition

Abbas Ghaddar, Philippe Langlais†, Ahmad Rashid and Mehdi Rezagholizadeh
Huawei Noah’s Ark Lab, Montreal Research Center, Canada
†RALI/DIRO, Université de Montréal, Canada
<EMAIL_ADDRESS><EMAIL_ADDRESS>
<EMAIL_ADDRESS><EMAIL_ADDRESS>

###### Abstract

In this work, we examine the ability of NER models to use contextual
information when predicting the type of an ambiguous entity. We introduce NRB,
a new testbed carefully designed to diagnose Name Regularity Bias of NER
models. Our results indicate that all state-of-the-art models we tested show
such a bias; BERT fine-tuned models significantly outperforming feature-based
(LSTM-CRF) ones on NRB, despite having comparable (sometimes lower)
performances on standard benchmarks.

To mitigate this bias, we propose a novel model-agnostic training method which
adds learnable adversarial noise to some entity mentions, thus enforcing
models to focus more strongly on the contextual signal, leading to significant
gains on NRB. Combining it with two other training strategies, data
augmentation and parameter freezing, leads to further gains.

##  1 Introduction

Recent advances in language model pre-training Peters et al. (2018); Devlin et
al. (2019); Liu et al. (2019) have greatly improved the performance of many
Natural Language Understanding (NLU) tasks. Yet, several studies McCoy et al.
(2019); Clark et al. (2019); Utama et al. (2020b) revealed that state-of-the-
art NLU models often make use of surface patterns in the data that do not
generalize well. Named-Entity Recognition (NER), a downstream task that
consists in identifying textual mentions and classifying them into a
predefined set of types, is no exception.

Gonzales, Louisiana

Gonzales${}^{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}LOC}}_{{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}PER}}$
is a small city in Ascension Parish, Louisiana.

Obama, Fukui

Obama${}^{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}LOC}}_{{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}PER}}$
is located in far southwestern Fukui Prefecture.

Patricia A. Madrid

Madrid${}^{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}PER}}_{{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}LOC}}$
won her first campaign in 1978 ..

Asda Jayanama

Asda${}^{{\color[rgb]{0,0,1}\definecolor[named]{pgfstrokecolor}{rgb}{0,0,1}PER}}_{{\color[rgb]{1,0,0}\definecolor[named]{pgfstrokecolor}{rgb}{1,0,0}ORG}}$
joined his brother, Surapong …

Figure 1: Examples extracted from Wikipedia (title in bold) that illustrate
name regularity bias in NER. Entities of interest are underlined, gold types
are in blue superscript, model predictions are in red subscript, and context
information is highlighted in purple. Models employed in this study disregard
contextual information and rely instead on some signal from the named-entity
itself.

The robustness of modern NER models has received considerable attention
recently Mayhew et al. (2019, 2020); Agarwal et al. (2020a); Zeng et al.
(2020); Bernier-Colborne and Langlais (2020). Name Regularity Bias Lin et al.
(2020); Agarwal et al. (2020b); Zeng et al. (2020) in NER occurs when a model
relies on a signal coming from the entity name, and disregards evidences
within the local context. Figure 1 shows examples where state-of-the-art
models Peters et al. (2018); Akbik et al. (2018); Devlin et al. (2019) fail to
exploit contextual information. For instance, the entity Gonzales in the first
sentence of the figure is wrongly recognized as a person, while the context
clearly signals that it is a location (city).

To better highlight this issue, we propose NRB, a testbed designed to
accurately diagnose name regularity bias of NER models by harvesting natural
sentences from Wikipedia that contain challenging entities, such as those in
Figure 1. This is different from previous works that evaluate models on
artificial data obtained by either randomizing Lin et al. (2020) or
substituting entities by ones from a pre-defined list Agarwal et al. (2020a).
NRB is compatible with any annotation scheme, and is intended to be used as an
auxiliary validation set.

We conduct experiments with the feature-based LSTM-CRF architecture Peters et
al. (2018); Akbik et al. (2018) and the BERT Devlin et al. (2019) fine-tuning
approach trained on standard benchmarks. The best LSTM-based model we tested
is able to correctly predict 38% of the entities in NRB. BERT-based models are
performing much better (+37%), even if they (slightly) underperform on in-
domain development and test sets. This mismatch in performance between NRB and
standard benchmarks indicates that context awareness of models is not rewarded
by existing benchmarks, thus justifying NRB as an additional validation set.

We further propose a novel architecture-agnostic adversarial training
procedure Miyato et al. (2016) in which learnable noise vectors are added to
named-entity words, weakening their signal, thus encouraging the model to pay
more attention to contextual information. Applying it to both feature-based
LSTM-CRF and fine-tuned BERT models leads to consistent gains on NRB (+13
points) while maintaining the same level of performance on standard
benchmarks.

The remainder of the paper is organized as follows. We discuss related works
in Section 2. We describe how we built NRB in Section 3, and its use in
diagnosing named-entity bias of state-of-the-art models in Section 4. In
Section 5, we present a novel adversarial training method that we compare and
combine with two simpler ones. We further analyze these training methods in
Section 6, and conclude in Section 7.

##  2 Related Work

Robustness and out-of-distribution generalization has always been a persistent
concern in deep learning applications such as computer vision Szegedy et al.
(2013); Recht et al. (2019), speech processing Seltzer et al. (2013); Borgholt
et al. (2020), and NLU Søgaard (2013); Hendrycks and Gimpel (2017); Ghaddar
and Langlais (2017); Yaghoobzadeh et al. (2019); Hendrycks et al. (2020). One
key challenge behind this issue in NLU is the tendency of models to quickly
leverage surface form features and annotation artifacts Gururangan et al.
(2018), which is often referred to as dataset biases Dasgupta et al. (2018);
Shah et al. (2020). We discuss related works along two axes: diagnosis and
mitigation.

###  2.1 Diagnosing Biais

A growing number of studies Zellers et al. (2018); Poliak et al. (2018); Geva
et al. (2019); Utama et al. (2020b); Sanh et al. (2020) are showing that NLU
models rely heavily on spurious correlations between output labels and surface
features (e.g. keywords, lexical overlap), impacting their generalization
performance. Therefore, considerable attention has been paid to design
diagnostic benchmarks where models relying on bias would perform poorly. For
instance, HANS McCoy et al. (2019), FEVER Symmetric Schuster et al. (2019),
and PAWS Zhang et al. (2019) are benchmarks that contain counterexamples to
well-known biases in the training data of textual entailment Williams et al.
(2017), fact verification Thorne et al. (2018), and paraphrase identification
Wang et al. (2018) respectively.

Naturally, many entity names have a strong correlation with a single type
(e.g. <Gonzales, PER> or <Madrid, LOC>). Recent works have noted that over-
relying on entity name information negatively impacts NLU tasks.
Balasubramanian et al. (2020) found that substituting named-entities in
standard test sets of natural language inference, coreference resolution, and
grammar error correction has a negative impact on those tasks. In political
claims detection Padó et al. (2019), Dayanik and Padó (2020) show that claims
made by frequently occurring politicians in the training data are better
recognized than those made by less frequent ones.

Recently, Zeng et al. (2020) and Agarwal et al. (2020b) conducted two separate
analyses on the decision making mechanism of NER models. Both works found that
context tokens do contribute to system performance, but that entity names play
a major role in driving high performances. Agarwal et al. (2020a) reported a
performance drop in NER models when entities in standard test sets are
substituted with other ones pulled from pre-defined lists. Concurrently, Lin
et al. (2020) conducted an empirical analysis on the robustness of NER models
in the open domain scenario. They show that models are biased by strong entity
name regularity, and train$\backslash$test overlap in standard benchmarks.
They observe a drop in performance of 34% when entity mentions are randomly
replaced by other mentions.

The aforementioned studies certainly demonstrate name regularity bias. Still,
in many cases the entity mention is the only key to infer its type, as in
"James won the league". Thus, randomly swapping entity names, as proposed by
Lin et al. (2020), typically introduces false positive examples, which
obscures observations. Furthermore, creating artificial word sequences
introduces a mismatch between the pre-training and the fine-tuning phases of
large-scale language models.

NER is also challenging because of compounding factors such as entity boundary
detection Zheng et al. (2019), rare words and emerging entities Strauss et al.
(2016), document-level context Durrett and Klein (2014), capitalization
mismatch Mayhew et al. (2019), unbalance datasets Nguyen et al. (2020), and
domain shift Alvarado et al. (2015); Augenstein et al. (2017). It is unclear
to us how randomizing mentions in a corpus, as proposed by Lin et al. (2020),
is interfering with these factors.

NRB gathers genuine entities that appear in natural sentences extracted from
Wikipedia. Examples are selected so that entity boundaries are easy to
identify, and their types can be inferred from the local context, thus
avoiding compounding many factors responsible for lack of robustness.

###  2.2 Mitigating Bias

The prevailing approach to address dataset biases consists in adjusting the
training loss for biased examples. A number of recent studies Clark et al.
(2019); Belinkov et al. (2019); He et al. (2019); Mahabadi et al. (2020);
Utama et al. (2020a) proposed to train a shallow model that exploits manually
designed biased features. A main model is then trained in an ensemble with
this pre-trained model, in order to discourage the main model from adopting
the naive strategy of the shallow one.

Adversarial training Miyato et al. (2016) is a regularization method which has
been shown to improve not only robustness Ebrahimi et al. (2018); Bekoulis et
al. (2018), but also generalization Cheng et al. (2019); Zhu et al. (2019) in
NLU. It builds on the idea of adding adversarial examples Goodfellow et al.
(2014); Fawzi et al. (2016) to the training set, that is, small perturbations
of the data that can change the prediction of a classifier. These
perturbations for NLP tasks are done at the token embedding level and are norm
bounded. Typically, adversarial training algorithms can be defined as a minmax
optimization problem wherein the adversarial examples are generated to
maximize the loss, while the model is trained to minimize it.

Belinkov et al. (2019) used adversarial training to mitigate the hypothesis-
only bias in textual entailment models. Clark et al. (2020) adversarially
trained a low and a high capacity model in an ensemble in order to ensure that
the latter model is focusing on patterns that should generalize better.
Dayanik and Padó (2020) used an extra adversarial loss in order to encourage a
political claims detection model to learn more from samples with infrequent
politician names. Le Bras et al. (2020) proposed an adversarial technique to
filter-out biased examples from training material. Models trained on the
filtered datasets show improved out-of-distribution performances on various
computer vision and NLU tasks.

Data augmentation is another strategy for enhancing robustness. It was
successfully used in Min et al. (2020) and Moosavi et al. (2020) to improve
textual entailment performances on the HANS benchmark. The former approach
proposes to append original training sentences with their corresponding
predicate-arguments triplets generated by a semantic role labelling tagger;
while the latter generates new examples by applying syntactic transformations
to the original training instances.

Zeng et al. (2020) created new examples by randomly replacing an entity by
another one of the same type that occurs in the training data. New examples
are considered valid if the type of the replaced entity is correctly predicted
by a NER model trained on the original dataset. Similarly, Dai and Adel (2020)
explored different entity substitution techniques for data augmentation
tailored to NER. Both studies conclude that data augmentation techniques based
on entity substitution improves the overall performances on low resource
biomedical NER.

Studies discussed above have the potential to mitigate name regularity bias of
NER models. Still, we are not aware of any dedicated work that shows it is so.
In this work, we propose ways of mitigating name regularity bias for NER,
including an elaborate adversarial method that enforces the model to capture
more signal from the context. Our methods do not require an extra training
stage, or to manually characterize biased features. They are therefore
conceptually simpler, and can potentially be combined to any of the discussed
techniques. Furthermore, our proposed methods are effective under both low and
high resource settings.

##  3 The NRB Benchmark

NRB is a diagnosing testbed exclusively dedicated to name regularity bias in
NER. To this end, it gathers named-entities that satisfy 4 criteria:

  1. 1.

Must be real-world entities within natural sentences $\rightarrow$ We select
sentences from Wikipedia articles.

  2. 2.

Must be compatible with any annotation scheme $\rightarrow$ We restrict our
focus on the 3 most common types found in NER benchmarks: person, location,
and organization.

  3. 3.

Boundary detection (segmentation) should not be a bottleneck $\rightarrow$ We
only select single word entities that start with a capital letter.

  4. 4.

Supporting evidences of the type must be restricted to local context only (a
window of 2 to 4 tokens) $\rightarrow$ We developed a primitive context-only
tagger to filter-out entities with no close-context signal.

Disambiguation page |
---|---
Bromwich (disambiguation)
Query term | Bromwich
Wikipedia article | Kenny Bromwich
Freebase type | PER
Sentence |
Round 5 of the 2013 NRL season Bromwich made his NRL debut for the Melbourne
Storm
Taggers |
weak supervision org (confidence: 0.97)
context-only per: 0.58, org: 0.30, loc: 0.12

Figure 2: Selection of a sentence in NRB.

The strategy used to gather examples in NRB is illustrated in Figure 2. We
first select Wikipedia articles that are listed in a disambiguation page.
Disambiguation pages group different topics that could be referred to by the
same query
term.111https://en.wikipedia.org/wiki/Wikipedia:Manual_of_Style/Disambiguation_pages.
The query term Bromwich in Figure 2 has its own disambiguation page that
contains a link to the city of West Bromwich, West Bromwich Albion Football
Club, and Kenny Bromwich the rugby league player.

We associate each article in a disambiguation page to the entity type found in
its corresponding Freebase page Bollacker et al. (2008), considering only
articles whose Freebase type can be mapped to a person, a location, or an
organization. We assume that occurrences of the query term within the article
are of this type. This assumption was found accurate in previous works on
Wikipedia distant supervision for NER Ghaddar and Langlais (2016, 2018). The
sentence in our example is extracted from the Kenny Bromwich article, whose
Freebase type can be mapped to a person. Therefore, we assume Bromwich in this
sentence to be a person.

To decide whether a sentence containing a query term is worth being included
in NRB, we rely on two NER taggers. One is a popular NER system which provides
a confidence score to each prediction, and which acts as a weak superviser,
the other is a context-only tagger we designed specifically (see section 3.1)
to detect entities with a strong signal from their local context. A sentence
is selected if the query term is incorrectly labeled with high confidence
(score $>$ 0.85) by the former tagger, while the latter one labels it
correctly with high confidence (a gap of at least 0.25 in probability between
the first and second predicted types). This is the case of the sentence in
Figure 2 where Bromwich is incorrectly labeled as an organisation by the weak
supervision tagger, however correctly labeled as a person by the context-only
tagger.

###  3.1 Implementation

We used the Stanford CoreNLP Manning et al. (2014) tagger as our weak
supervision tagger and developed a simple yet efficient method to build a
context-only tagger. For this, we first applied the Stanford tagger to the
entire Wikipedia dump and replaced all entity mentions identified by their
tag. Then, we train a 5-gram language model on the resulting corpus using
kenLM Heafield (2011). Figure 3 illustrates how this model is deployed as an
entity tagger: the mention is replaced by an empty slot and the language model
is queried for each type. We rank the tags using the perplexity score given by
the model to the resulting sentences, then we normalize those scores to get a
probability distribution over types.

Obama is located in far southwestern Fukui Prefecture.

$<$?$>$ is located in far southwestern Fukui Prefecture.

{LOC: 0.61, ORG: 0.28, PER: 0.11}

Figure 3: Illustration of a language model used as a context-only tagger.

We downloaded the Wikipedia dump of June 2020, which contains 30k
disambiguation pages. These pages contain links to 263k articles, where only
107k (40%) of them have a type in Freebase that can be mapped to the 3 types
of interest. The Stanford tagger identified 440k entities that match the query
term of the disambiguation pages. The thresholds discussed previously were
chosen to select around 5000 of the most challenging examples in terms of name
regularity bias. This figure aligns with the number of entities present in the
test set of the well-studied CoNLL benchmark Tjong Kim Sang and De Meulder
(2003).

We assessed the annotation quality, by asking a human to filter out noisy
examples. A sentence was removed if it contains an annotation error, or if the
type of the query term cannot be inferred from the local context. Only 1.3% of
the examples where removed, which confirms the accuracy of our automatic
procedure. NRB is composed of 5275 examples, and each sentence contains a
single annotation (see Figure 1 for examples).

Model | CoNLL | OntoNotes
---|---|---
Dev | Test | NRB | WTS | Dev | Test | NRB | WTS
Feature-based
Flair-LSTM | - | 93.03 | 27.56 | 99.58 | - | 89.06 | 33.67 | 93.98
ELMo-LSTM | 96.69 | 92.47 | 31.65 | 98.24 | 88.31 | 89.38 | 34.34 | 94.90
BERT-LSTM | 95.94 | 91.94 | 38.34 | 98.08 | 86.12 | 87.28 | 43.07 | 92.04
Fine-tuning
BERT-base | 96.18 | 92.19 | 75.54 | 98.67 | 87.23 | 88.19 | 75.34 | 94.22
BERT-large | 96.90 | 92.86 | 75.55 | 98.51 | 89.26 | 89.93 | 75.41 | 95.06
Table 1: Mention level F1 scores of models on CoNLL and OntoNotes, as well as
on NRB and WTS.

###  3.2 Control Set (WTS)

In addition to NRB, we collected a set of domain control sentences — called
WTS for Witness — that contain the very same query terms selected in NRB, but
which were correctly labeled by both the Stanford (score $>$ 0.85) and the
context-only taggers. We selected examples with a small gap ($<$ 0.1) between
the first and second ranked type assigned to the query term by the latter
tagger. Thus, examples in WTS should be easy to tag. For example, because
Obama the Japanese city (see Figure 3) is selected among the query terms in
NRB, we added an instance of Obama the president.

Performing poorly on such examples222That is, a system that fail to tag Obama
the president as a person. indicates a domain shift between NRB (Wikipedia)
and whatever dataset a model is trained on (we call it the in-domain corpus).
WTS is composed of 5192 sentences that have also been manually checked.

##  4 Diagnosing Bias

###  4.1 Data

To be comparable with state-of-the-art models, we consider two standard
benchmarks for NER: CoNLL-2003 Tjong Kim Sang and De Meulder (2003) and
OntoNotes 5.0 Pradhan et al. (2012) which include 4 and 18 types of named-
entities respectively. OntoNotes is 4 times larger than CoNLL, and both
benchmarks mainly cover the news domain. We run experiments on the official
train/dev/test splits, and report mention-level F1 scores, following previous
works. Since in NRB, there is only one entity per sentence to annotate, a
system is evaluated on its ability to correctly identify the boundaries of
this entity and its type. When we train on OntoNotes (18 types) and evaluate
on NRB (3 types), we perform type mapping using the scheme of Augenstein et
al. (2017).

###  4.2 Systems

Following Devlin et al. (2019), we term all approaches that learn the encoder
from scratch as feature-based, as opposed to the ones that fine-tune a pre-
trained model for the downstream task. We conduct experiments using 3 feature-
based and 2 fine-tuning approaches for NER:

  * •

Flair-LSTM An LSTM-CRF model that uses Flair Akbik et al. (2018)
contextualized embeddings as main features.

  * •

ELMo-LSTM The LSTM-CRF tagging model of Peters et al. (2018) that uses ELMo
contextualized embeddings at the input layer.

  * •

BERT-LSTM Similar to the previous model, but replacing ELMo by a
representation gathered from the last four layers of BERT.

  * •

BERT-base The fine-tuning approach proposed by Devlin et al. (2019) using the
BERT-base model.

  * •

BERT-large The fine-tuning approach using the BERT-large model.

We used Flair-LSTM off-the-shelf,333https://github.com/flairNLP/flair and re-
implemented other approaches using the default settings proposed in the
respective papers. For our reimplementations, we used early stopping based on
performance on the development set, and report average performance over 5
runs. For BERT-based solutions, we adopt spanBERT Joshi et al. (2020) as a
backbone model since it was found by Li et al. (2020) to perform better on
NER.

###  4.3 Results

Table 1 shows the mention level F1 score of the systems considered. Flair-LSTM
and BERT-large are the best performing models on in-domain test sets, the
maximum gap with other models being 1.1 and 2.7 on CoNLL and OntoNotes
respectively. These figures are in line with previous works. What is more
interesting is the performance on NRB. Feature-based models do poorly, Flair-
LSTM underperforms compared to other models (F1 score of 27.6 and 33.7 when
trained on CoNLL and OntoNotes respectively). Fine-tuned BERT models clearly
perform better (around 75), but far from in-domain results (92.9 and 89.9 on
CoNLL and OntoNotes respectively). Domain shift is not a reason for those
results, since the performances on WTS are rather high (92 or higher).
Furthermore, we found that the boundary detection (segmentation) performance
on NRB is above 99.2% across all settings. Since errors made on NRB are
neither due to segmentation nor to domain shift, they must be imputed to name
regularity bias of models.

It is worth noting that BERT-LSTM outperforms ELMo-LSTM on NRB, despite
underperforming on in-domain test sets. This may be because BERT was pre-
trained on Wikipedia (same domain of NRB), while ELMo embeddings were trained
on the One Billion Word corpus Chelba et al. (2014). Also, we observe that
switching from BERT-base to BERT-large, or training on 4 times more data
(CoNLL versus OntoNotes) does not help on NRB. This suggests that name
regularity bias is neither a data nor a model capacity issue.

###  4.4 Feature-based vs. Fine-tuning

In this section, we analyze reasons for the drastic superiority of fined-tuned
models on NRB. First, the large gap between BERT-LSTM and BERT-base on NRB
suggests that this is not related to the representations being used at the
input layer.

Second, we tested several configurations of ELMo-LSTM where we scale up the
number of LSTM layers and hidden units. We observed a degradation of
performance on dev, test and NRB sets, mostly due to over-parameterized
models. We also trained 9, 6 and 4 layers BERT-base models,444We used early
exit Xin et al. (2020) at the $k^{th}$ layer. and still noticed a large
advantage of BERT models on NRB.555The 4-layer model has 53M parameters and
performs 52% on NRB. This suggests that the higher capacity of BERT alone can
not explain all the gains.

Third, since by design, evidences on the entity type in NRB reside within the
local context, it is unlikely that gains on this set come from the ability of
Transformers Vaswani et al. (2017) to better handle long dependencies than
LSTM Hochreiter and Schmidhuber (1997). To further validate this statement, we
fine-tuned BERT models with randomly initialized weights, except the embedding
layer. We noticed that this time, the performances on NRB fall into the same
range of those of feature-based models, and a drastic decrease (12-15%) on
standard benchmarks. These observations are in keeping with results from
Hendrycks et al. (2020) on the out-of-distribution robustness of fine-tuning
pre-trained transformers, and also confirms observations made by Agarwal et
al. (2020b).

From these analyses, we conclude that the Masked Language Model (MLM)
objective Devlin et al. (2019) that the BERT models were pre-trained with is a
key factor driving superior performances of the fine-tuned models on NRB. In
most cases, the target word is masked or randomly selected, therefore the
model must rely on the context to predict the correct target, which is what a
model should do to correctly predict the type of entities in NRB. We think
that in fine-tuning, training for a few epochs with a small learning rate,
helps the model to preserve the contextual behaviour induced by the MLM
objective.

Nevertheless, fine-tuned models recording at best an F1 score of 75.6 on NRB
do show some name regularity bias, and fail to capture useful local contextual
information.

Figure 4: Illustration of our adversarial method applied on the entity New
York. First, we generate a noisy type (PER), and then add a learnable noise
embedding (LOC$\rightarrow$PER) to the input representation of that entity.
This will make entity patterns (hashed rectangles) unreliable for the model,
hence forcing it to collect evidences (dotted arrow) from the context. The
noise embedding matrix and the noise label projection layer weights (dotted
rectangle) are trained independently from the model parameters.

##  5 Mitigating Bias

In this section, we investigate training procedures that are designed to
enhance the contextual awareness of a model, leading to a better performance
on NRB without impacting in-domain performance. These training procedures are
not supposed to use any external data. In fact, NRB is only used as a
diagnosing corpus, once the model is trained. We propose 3 training procedures
that can be combined, two of them are architecture-agnostic, and one is
specific to fine-tuning BERT.

###  5.1 Entity Masking

Inspired by the masking strategy applied during the pre-training phase of
BERT, we propose a data augmentation approach that introduces a special [MASK]
token in some of the training examples. Specifically, we search for entities
in the training material that are preceded or followed by 3 non-entity words.
This criterion applies to 35% and 39% of entities in the training data of
CoNLL and OntoNotes respectively. For each such entity, we create a new
training example (new sentence) by replacing the entity by [MASK], thus
forcing the model to infer the type of masked tokens from the context. We call
this procedure mask.

###  5.2 Parameter Freezing

Another simple strategy, specific to fine-tuning BERT, consists of freezing
part of the network. More precisely, we freeze the bottom half of BERT,
including the embedding layer. The intuition is to preserve part of the
predicting-by-context mechanism that BERT has acquired during the pre-training
phase. This training procedure is expected to enforce the contextual ability
of the model, thus adding to our analysis on the critical role of the MLM
objective in pre-training BERT. We name this method freeze.

###  5.3 Adversarial Noise

We propose an adversarial learning algorithm that makes entity type patterns
in the input representation less reliable for the model, thus enforcing it to
rely more aggressively on the context. To do so, we add a learnable
adversarial noise vector (only) to the input representation of entities. We
refer to this method as adv.

Let $T=\\{t_{1},t_{2},\ldots,t_{K}\\}$ be a predefined set of types such as
PER, LOC, and ORG in our case. Let $x=x_{1},x_{2},\ldots,x_{n}$ be the input
sequence of length $n$, $y=y_{1},y_{2},\ldots,y_{n}$ be the gold label
sequence following the IOB666Naturally applies to other schemes, such as BILOU
that Ratinov and Roth (2009) found more informative. tagging scheme, and
$y^{\prime}=y^{\prime}_{1},y^{\prime}_{2},\ldots,y^{\prime}_{n}$ be a sequence
obtained by adding noise to $y$ at the mention-level, that is, by randomly
replacing the type of mentions in $y$ with some noisy type sampled from $T$.

Let $\mathcal{Y}_{ij}(t)=y_{i},\ldots,y_{j}$ be a mention of type $t\in T$,
spanning the sequence of indices $i$ to $j$ in $y$. We derive a noisy mention
$\mathcal{Y^{\prime}}_{ij}$ in $y^{\prime}$ from $\mathcal{Y}_{ij}(t)$ as
follows:

$\mathcal{Y}^{\prime}_{ij}=\begin{cases}\mathcal{Y}_{ij}(t^{\prime})&{p\sim
U(0,1)\leq\lambda}\\\ &t^{\prime}\sim\underset{\gamma\in
T\setminus{\\{t\\}}}{\text{Cat}}(\gamma|\xi=\frac{1}{K-1})\\\
\mathcal{Y}_{ij}(t)&\text{otherwise}\\\ \end{cases}$

where $\lambda$ is a threshold parameter, $U(0,1)$ refers to the uniform
distribution in the range [0,1], Cat$(\gamma|\xi=\frac{1}{K-1})$ is the
categorical distribution whose outcomes are equally likely with the
probability of $\xi$, and the set
$T\setminus{\\{t\\}}=\\{t^{\prime}:t^{\prime}\in T\wedge t^{\prime}\neq t\\}$
stands for the set $T$ excluding type $t$.

The above procedure only applies to the entities which are preceded or
followed by 3 context words. For instance, in Figure 4, we produce a noisy
type for New York (PER), but not for John ($p>\lambda$). Also, note that we
generate a different sequence $y^{\prime}$ from $y$ at each training epoch.

Next, we define a learnable noisy embedding matrix
$E^{\prime}\in\mathbb{R}^{m\times d}$ where $m=|T|\times(|T|-1)$ is the number
of valid type switching possibilities, and $d$ is the dimension of the input
representations of $x$. For each token with a noisy label, we add the
corresponding noisy embedding to its input representation. For other tokens,
we simply add a zero vector of size $d$. As depicted in Figure 4, the noisy
type of the entity New York is PER, therefore we add the noise embedding at
index $LOC\rightarrow{PER}$ to its input representation.

Then, the input representation of the sequence is fed to an encoder followed
by an output layer, such as LSTM-CRF in Peters et al. (2018), or BERT-Softmax
in Devlin et al. (2019). First, we extend the aforementioned models by
generating an extra logit $f^{\prime}$ using a projection layer parametrized
by $W^{\prime}$ and followed by a softmax function. As shown in Figure 4, for
each token the model produces two logits relative to the true and noisy tags.
Then, we train the entire model to minimize two losses: $L_{true}(\theta)$ and
$L_{noisy}(\theta^{\prime})$, where $\theta$ is the original set of parameters
and $\theta^{\prime}=\\{E^{\prime},W^{\prime}\\}$ is the extra set we added
(dotted boxes in Figure 4). $L_{true}(\theta)$ is the regular loss on the true
tags, while $L_{noisy}(\theta^{\prime})$ is the loss on the noisy tags defined
as follows:

$L_{\text{noisy}}(\theta^{\prime})=\sum_{i=1}^{n}\mathbbm{1}(y^{\prime}_{i}\neq
y_{i})\text{ CE}(f^{\prime}_{i},y^{\prime}_{i})$

where CE is the cross-entropy loss function. Both losses are minimized using
gradient descent. It is worth mentioning that $\lambda$ is the only hyper-
parameter of our adv method. It controls how often noisy embeddings are added
during training. Higher values of $\lambda$ increase the amount of uncertainty
around salient patterns in the input representation of entities, hence
preventing the model from overfitting those patterns, and therefore pushing it
to rely more on context information. We tried values of $\lambda$ between
$0.3$ and $0.9$, and found $\lambda=0.8$ to be the best one based on CoNLL and
OntoNotes development sets.

###  5.4 Results

We trained models on CoNLL and OntoNotes, and evaluated them on their
respective test set.777Performances on dev show very similar trends. Recall
that NRB and WTS are only used as auxiliary diagnosing sets. Table 2 shows the
impact of our training methods when fine-tuning the BERT-large model (the one
that performs best on NRB).

First, we observe that each training method significantly improves the
performance on NRB. Adding adversarial noise is notably the best performing
method on NRB, with an additional gain of 10.5 and 10.4 F1 points over the
respective baselines. On the other hand, we observe minor variations on in-
domain test sets, as well as on WTS. The paired sample t-test Cohen (1996)
confirms that these variations are not statistically significant ($p>0.05$).
After all, the number of decisions that differ between the baseline and the
best model on a given in-domain set is less than 20.

Method | CoNLL | OntoNotes
---|---|---
Test | nrb | wts | Test | nrb | wts
BERT-lrg | 92.8 | 75.6 | 98.6 | 89.9 | 75.4 | 95.1
+mask | 92.9 | 82.9 | 98.4 | 89.8 | 77.3 | 96.5
+freeze | 92.7 | 83.1 | 98.4 | 89.9 | 79.8 | 96.0
+adv | 92.7 | 86.1 | 98.3 | 90.1 | 85.8 | 95.2
+f&m | 92.8 | 85.5 | 97.8 | 89.9 | 80.6 | 95.9
+a&m | 92.8 | 87.7 | 98.1 | 89.7 | 87.6 | 95.9
+a&f | 92.7 | 88.4 | 98.2 | 90.0 | 88.1 | 95.7
+a&m&f | 92.8 | 89.7 | 97.9 | 89.9 | 88.8 | 95.6
Table 2: Impact of training methods on BERT-large models fine-tuned on CoNLL
or OntoNotes.

Second, we observe that combining methods always leads to improvements on NRB;
the best configuration being when we combine all 3 methods. It is interesting
to note that combining training methods leads to a performance on NRB which
does not depend much on the training set used: CoNLL (89.7) and OntoNotes
(88.8). This suggests that name regularity bias is a modelling issue, and not
the effect of factors such as training data size, domain, or type granularity.

Method | CoNLL | OntoNotes
---|---|---
Test | nrb | wts | Test | nrb | wts
E-LSTM | 92.5 | 31.7 | 98.2 | 89.4 | 34.3 | 94.9
+mask | 92.4 | 40.8 | 97.5 | 89.3 | 38.8 | 95.3
+adv | 92.4 | 42.4 | 97.8 | 89.4 | 40.7 | 95.0
+a&m | 92.4 | 45.7 | 96.8 | 89.3 | 46.6 | 93.7
Table 3: Impact of training methods on the ELMo-LSTM trained on CoNLL or
OntoNotes.

In order to validate that our training methods are not specific to the fine-
tuning approach, we replicated the same experiments with the ELMo-LSTM. Table
3 shows the performances of the mask and adv procedures (the freeze method
does not apply here). The results are in line with those observed with BERT-
large: significant gains on NRB of 14 and 12 points for CoNLL and OntoNotes
models respectively, and no statistically significant changes on in-domain
test sets. Again, combining training methods leads to systematic gains on NRB
(13 points on average). Differently from fine-tuning BERT, we observe a slight
drop in performance of 1.2% on WTS when both methods are used.

The performance of ELMo-LSTM on NRB does not rival with the one obtained by
fine-tuning the BERT-large model, which confirms that BERT is a key factor to
enhance robustness, even if in-domain performance is not necessarily rewarded
McCoy et al. (2019); Hendrycks et al. (2020).

##  6 Analysis

So far, we have shown that state-of-the-art models do suffer from name
regularity bias, and we proposed model-agnostic training methods which are
able to mitigate this bias to some extent. In Section 6.1, we provide further
evidences that our training methods force the BERT-large model to better
concentrate on contextual cues. In Section 6.2, we replicate the evaluation
protocol of Lin et al. (2020) in order to clear out the possibility that our
training methods are only valid on NRB. Last, we perform extensive experiments
on name regularity bias under low resource (Section 6.3) and multilingual
(Section 6.4) settings.

###  6.1 Attention Heads

We leverage the attention map of BERT to better understand how our method
enhances context encoding. To this end, we calculate the average number of
attention heads that point to the entity mentions being predicted at each
layer. We conduct this experiment on NRB with the BERT-large model (24 layers
with 16 attention heads at each layer) fine-tuned on CoNLL.

Figure 5: Average number of attention heads (y-axis) pointing to NRB entity
mentions at each layer (x-axis) of the BERT-large model fine-tuned on CoNLL.

At each layer, we average the number of heads which have their highest
attention weight (argmax) pointing to the entity name.888We used the weights
of the first sub-token since NRB only contains single word entities. Figure 5
shows the average number of attention heads that point to an entity mention in
the BERT-large model fine-tuned without our methods, with the adversarial
noise method (adv), and with all three methods.

We observe an increasing number of heads pointing to entity names when we get
closer to the output layer: at the bottom layers (left part of the figure)
only a few heads are pointing to entity names, in contrast to the last 2
layers (right part) where almost all heads do so. This observation is inline
with Jawahar et al. (2019) who show that bottom and intermediate BERT layers
mainly encode lexical and syntactic information, while top layers represent
task-related information. Our training methods lead to less heads at top
layers pointing to entity mentions, suggesting the model is focusing more on
contextual information.

###  6.2 Random Permutations

Following the protocol described in Lin et al. (2020), we modified dev and
test sets of standard benchmarks by randomly permuting dataset-wise mentions
of entities, keeping the types untouched. For instance, the span of a specific
mention of a person can be replaced by a span of a location, whenever it
appears in the dataset. These randomized tests are highly challenging, as
discussed in Section 2, since here the context is the only available clue to
solve the task, and many false positive examples are introduced that way.

Method |  $\pi($dev$)$ |  $\pi($test$)$
---|---|---
BERT-large | 23.45 | 25.46
+adv | 31.98 | 31.99
+adv&mask | 35.02 | 34.09
+adv&mask&freeze | 40.39 | 38.62
Table 4: F1 scores of BERT-large models fine-tuned on CoNLL and evaluated on
randomly permuted versions of the dev and test sets: $\pi($dev$)$ and
$\pi($test$)$.

Table 4 shows the results of the BERT-large model fine-tuned on CoNLL and
evaluated on the permuted in-domain dev and test sets. F1 scores are much
lower here, confirming this is a hard testbed, but they do provide evidences
of the named-regularity bias of BERT. Our training methods improve the model
F1 score by 17% and 13% on permuted dev and test sets respectively, an
increase much inline with what we observed on NRB.

###  6.3 Low Resource Setting

Similarly to Zhou et al. (2019); Ding et al. (2020), we simulate a low
resource setting by randomly sampling tiny subsets of the training data. Since
our focus is to measure the contextual learning ability of models, we first
selected sentences of CoNLL training data that contain at least one entity
followed or preceded by 3 non-entity words.

Figure 6: Performance on NRB of BERT-large models as a function of the number
of sentences used to fine-tune them.

Then, we randomly sampled $k\in\\{100,500,1000,2000\\}$
sentences999$\\{0.7,3.5,7.1,14.3\\}$% of the training sentences. with which we
fine-tuned BERT-large. Figure 6 shows the performance of the resulting models
on NRB. Expectedly, F1 scores of models fine-tuned with few examples are
rather low on NRB as well as on the in-domain test set. Not shown in Figure 6,
fine-tuning on 100 and 2000 sentences leads to performance of 14% and 45%
respectively on the CoNLL test set. Nevertheless, we observe that our training
methods, and adv in particular, improve performances on NRB even under
extremely low resource settings. On CoNLL test and WTS sets, scores vary in a
range of $\pm 0.5$ and $\pm 0.7$ respectively when our methods are added to
BERT-large.

###  6.4 Multilingual Setting

####  6.4.1 Experimental Protocol

For in-domain data, we use the German, Spanish, and Dutch CoNLL-2002 Tjong Kim
Sang (2002) NER datasets. Those benchmarks — also from the news domain — come
with a train/dev/test split, and the training material is comparable in size
to the English CoNLL dataset. In addition, we experiment with four non CoNLL
benchmarks: Finnish Luoma et al. (2020), Danish Hvingelby et al. (2020),
Croatian Ljubešić et al. (2018), and Afrikaans Eiselen (2016) data. These
corpora have more diversified text genres, yet mainly follow the CoNLL
annotation scheme.101010The Finnish data is tagged with EVENT, PRODUCT and
DATE in addition to the CoNLL 4 classes. Finnish and Afrikaans datasets have
comparable size to English CoNLL, Danish is 60% smaller, while the Croatian is
twice larger. We use the provided train/dev/test splits for Danish and
Finnish, while we randomly split (80/10/10) the Croatian and Afrikaans
datasets.

Since NRB and WTS are in English, we designed a simple yet generic method for
projecting them to another language. First, both test sets are translated to
the target language using an online translation service. In order to ensure a
high quality corpus, we eliminate a sentence if the BLEU score Papineni et al.
(2002) between the original (English) sentence and the back translated one is
below 0.65.

| NRB | WTS |  | NRB | WTS
---|---|---|---|---|---
de | 37% | 44% | fi | 53% | 62%
es | 20% | 22% | da | 19% | 24%
nl | 20% | 24% | hr | 39% | 48%
|  |  | af | 26% | 32%
Table 5: Percentage of translated sentences from NRB and WTS discarded for
each language.

Model | German | Spanish | Dutch | Finnish | Danish | Croatian | Afrikaans
---|---|---|---|---|---|---|---
test | nrb | wts | test | nrb | wts | test | nrb | wts | test | nrb | wts | test | nrb | wts | test | nrb | wts | test | nrb | wts
Feature-based
BERT-LSTM | 78.9 | 36.4 | 84.2 | 85.6 | 59.9 | 90.8 | 84.9 | 45.4 | 85.7 | 76.0 | 38.9 | 84.5 | 76.4 | 42.6 | 78.1 | 78.0 | 28.4 | 79.3 | 76.2 | 39.7 | 65.8
+adv | 78.2 | 44.1 | 82.8 | 85.0 | 65.8 | 90.2 | 84.3 | 57.8 | 83.5 | 75.1 | 52.9 | 81.0 | 75.4 | 47.2 | 76.9 | 77.5 | 35.2 | 75.5 | 75.7 | 42.3 | 63.3
+adv&mask | 78.1 | 47.6 | 82.9 | 84.9 | 72.2 | 88.7 | 84.0 | 62.8 | 83.5 | 74.6 | 54.3 | 81.8 | 75.1 | 48.4 | 76.6 | 76.9 | 36.8 | 76.7 | 75.1 | 52.8 | 63.1
Fine-tuning
BERT-base | 83.8 | 64.0 | 93.3 | 88.0 | 72.3 | 93.9 | 91.8 | 56.1 | 92.0 | 91.3 | 64.6 | 91.9 | 83.6 | 56.6 | 86.2 | 89.7 | 54.7 | 95.6 | 80.4 | 54.3 | 91.6
+adv | 83.7 | 68.9 | 93.6 | 87.9 | 75.9 | 93.9 | 91.9 | 58.3 | 91.8 | 90.2 | 66.4 | 92.5 | 82.7 | 58.4 | 86.5 | 89.5 | 57.9 | 95.5 | 79.7 | 60.2 | 92.1
+a&m&f | 83.2 | 73.3 | 94.0 | 87.4 | 81.6 | 93.7 | 91.2 | 63.6 | 91.0 | 89.8 | 67.4 | 92.7 | 82.3 | 63.1 | 85.4 | 88.8 | 59.6 | 94.9 | 79.4 | 64.2 | 91.6

Table 6: Mention level F1 scores of 7 multilingual models trained on their
respective training data, and tested on their respective in-domain test, NRB,
and WTS sets.

Table 5 reports the percentage of discarded sentences for each language. While
for the Finnish (fi), Croatian (hr) and German (de) languages we remove a
large proportion of sentences, we found our translation approach more simple
and systematic than generating an NRB corpus from scratch for each language.
The latter approach depends on the robustness of the weak tagger, the number
of Wikipedia articles and disambiguation pages per language, as well as the
existence of type information. This is left as future work.

For experiments with fine-tuning, we use language-specific BERT
models111111Language-specific models have been reported more accurate than
multilingual ones in a monolingual setting Martin et al. (2019); Le et al.
(2020); Delobelle et al. (2020); Virtanen et al. (2019). for German Chan et
al. (2020), Spanish Canete et al. (2020), Dutch de Vries et al. (2019),
Finnish Virtanen et al. (2019),
Danish121212https://github.com/botxo/nordic_bert, Croatain Ulčar and Robnik-
Šikonja (2020), while we use mBERT Devlin et al. (2019) for Afrikaans.

For feature-based approaches, we use the same architecture for ELMo-LSTM
Peters et al. (2018) except that we replace English word embeddings by
language-specific ones: FastText Bojanowski et al. (2017) for static
representations, and the aforementioned BERT-base models for contextualized
ones.

####  6.4.2 Results

Table 6 reports the performances on test, NRB, and WTS sets for both feature-
based and fine-tuning approaches with and without our training methods. We
used the hyper-parameters of the English CoNLL experiments with no further
tuning. We selected the best performing models based on development sets
score, and report average results on 5 runs.

Mainly due to implementation details and hyper-parameter settings, our fine-
tuned BERT-base models perform better on the CoNLL test sets for German (83.8
vs. 80.4) and Dutch (91.8 vs. 90.0) and slightly worse on Spanish (88.0 vs.
88.4) compared to the results reported in their respective BERT papers.

Consistent with the results obtained on English for feature-based (Table 1)
and fine-tuned (Table 3) models, the latter approach performs better on NRB,
although by a smaller margin compared to English (+37%). More precisely, we
observe a gain of +28% and +26% on German and Croatian respectively, and a
gain ranging between 11% and 15% for other languages.

Nevertheless, our training methods lead to systematic and often drastic
improvements on NRB coupled with a statistically non significant overall
decrease on in-domain test sets. They do however incur a slight but
significant drop of around 2 F1 score points on WTS for feature-based models.
Similar to what was previously observed, the best scores on NRB are obtained
by BERT models when the training methods are combined. For the Dutch language,
we observe that once trained with our methods, the type of models used
(feature-based versus BERT fine-tuned) leads to much less difference on NRB.

Altogether, these results demonstrate that name regularity bias is not
specific to a particular language, even if its degree of severity varies from
one language to another, and that the training methods proposed notably
mitigate this bias.

##  7 Conclusion

In this work, we focused on the name regularity bias of NER models, a problem
first discussed in Lin et al. (2020). We propose NRB, a benchmark we
specifically designed to diagnose such a bias. As opposed to existing
strategies devised to measure it, NRB is composed of real sentences with easy
to identify mentions.

We show that current state-of-the-art models, perform from poorly (feature-
based) to decently (fined-tuned BERT) on NRB. In order to mitigate this bias,
we propose a novel adversarial training method based on adding some learnable
noise vectors to entity words. These learnable vectors encourage the model to
better incorporate contextual information. We demonstrate that this approach
greatly improves the contextual ability of existing models, and that it can be
combined with other training methods we proposed. Significant gains are
observed in both low-resource and multilingual settings. To foster research on
NER robustness, we encourage others to report results on NRB and
WTS.131313English and multilingual NRB and WTS are available at
http://rali.iro.umontreal.ca/rali/?q=en/wikipedia-nrb-ner

This study opens up new avenues of investigations. Conducting a large-scaled
multilingual experiment, characterizing the name regularity bias of more
diversified morphological language families is one of them, possibly
leveraging massively multilingual resources such as WikiAnn Pan et al. (2017),
Polyglot-NER Al-Rfou et al. (2015), or Universal Dependencies Nivre et al.
(2016). We can also develop a more challenging NRB by selecting sentences with
multi-word entities.

Also, non-sequential labelling approaches for NER like the ones of Li et al.
(2020); Yu et al. (2020) have reported impressive results on both flat and
nested NER. We plan to measure their bias on NRB and study the benefits of
applying our training methods to those approaches. Finally, we want to
investigate whether our adversarial training method can be successfully
applied to other NLP tasks.

##  8 Acknowledgments

We are grateful to the reviewers of this work for their constructive comments
that greatly contributed to improving this paper.

## References

  * Agarwal et al. (2020a) Oshin Agarwal, Yinfei Yang, Byron C Wallace, and Ani Nenkova. 2020a.  Entity-Switched Datasets: An Approach to Auditing the In-Domain Robustness of Named Entity Recognition Models.  _arXiv preprint arXiv:2004.04123_.
  * Agarwal et al. (2020b) Oshin Agarwal, Yinfei Yang, Byron C Wallace, and Ani Nenkova. 2020b.  Interpretability analysis for named entity recognition to understand system predictions and how they can improve.  _arXiv preprint arXiv:2004.04564_.
  * Akbik et al. (2018) Alan Akbik, Duncan Blythe, and Roland Vollgraf. 2018.  Contextual string embeddings for sequence labeling.  In _Proceedings of the 27th International Conference on Computational Linguistics_ , pages 1638–1649.
  * Al-Rfou et al. (2015) Rami Al-Rfou, Vivek Kulkarni, Bryan Perozzi, and Steven Skiena. 2015.  Polyglot-ner: Massive multilingual named entity recognition.  In _Proceedings of the 2015 SIAM International Conference on Data Mining_ , pages 586–594. SIAM.
  * Alvarado et al. (2015) Julio Cesar Salinas Alvarado, Karin Verspoor, and Timothy Baldwin. 2015.  Domain adaption of named entity recognition to support credit risk assessment.  In _Proceedings of the Australasian Language Technology Association Workshop 2015_ , pages 84–90.
  * Augenstein et al. (2017) Isabelle Augenstein, Leon Derczynski, and Kalina Bontcheva. 2017.  Generalisation in named entity recognition: A quantitative analysis.  _Computer Speech & Language_, 44:61–83.
  * Balasubramanian et al. (2020) Sriram Balasubramanian, Naman Jain, Gaurav Jindal, Abhijeet Awasthi, and Sunita Sarawagi. 2020.  What’s in a name? are bert named entity representations just as good for any other name?  _arXiv preprint arXiv:2007.06897_.
  * Bekoulis et al. (2018) Giannis Bekoulis, Johannes Deleu, Thomas Demeester, and Chris Develder. 2018.  Adversarial training for multi-context joint entity and relation extraction.  In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , pages 2830–2836.
  * Belinkov et al. (2019) Yonatan Belinkov, Adam Poliak, Stuart M Shieber, Benjamin Van Durme, and Alexander M Rush. 2019.  On adversarial removal of hypothesis-only bias in natural language inference.  In _Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (SEM 2019)_ , pages 256–262.
  * Bernier-Colborne and Langlais (2020) Gabriel Bernier-Colborne and Phillippe Langlais. 2020.  HardEval: Focusing on Challenging Tokens to Assess Robustness of NER.  In _Proceedings of The 12th Language Resources and Evaluation Conference_ , pages 1697–1704, Marseille, France. European Language Resources Association.
  * Bojanowski et al. (2017) Piotr Bojanowski, Edouard Grave, Armand Joulin, and Tomas Mikolov. 2017.  Enriching word vectors with subword information.  _Transactions of the Association for Computational Linguistics_ , 5:135–146.
  * Bollacker et al. (2008) Kurt Bollacker, Colin Evans, Praveen Paritosh, Tim Sturge, and Jamie Taylor. 2008\.  Freebase: a collaboratively created graph database for structuring human knowledge.  In _Proceedings of the 2008 ACM SIGMOD international conference on Management of data_ , pages 1247–1250.
  * Borgholt et al. (2020) Lasse Borgholt, Jakob D Havtorn, Anders Søgaard Zeljko Agic, Lars Maaløe, and Christian Igel. 2020.  Do end-to-end speech recognition models care about context?  In _Proc. of Interspeech_.
  * Canete et al. (2020) José Canete, Gabriel Chaperon, Rodrigo Fuentes, and Jorge Pérez. 2020.  Spanish pre-trained bert model and evaluation data.  _PML4DC at ICLR_ , 2020.
  * Chan et al. (2020) Branden Chan, Stefan Schweter, and Timo Möller. 2020.  German’s next language model.  _arXiv preprint arXiv:2010.10906_.
  * Chelba et al. (2014) Ciprian Chelba, Tomas Mikolov, Mike Schuster, Qi Ge, Thorsten Brants, Phillipp Koehn, and Tony Robinson. 2014.  One Billion Word Benchmark for Measuring Progress in Statistical Language Modeling.  In _Fifteenth Annual Conference of the International Speech Communication Association_.
  * Cheng et al. (2019) Yong Cheng, Lu Jiang, and Wolfgang Macherey. 2019.  Robust neural machine translation with doubly adversarial inputs.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 4324–4333.
  * Clark et al. (2019) Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2019.  Don’t take the easy way out: Ensemble based methods for avoiding known dataset biases.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 4060–4073.
  * Clark et al. (2020) Christopher Clark, Mark Yatskar, and Luke Zettlemoyer. 2020.  Learning to model and ignore dataset bias with mixed capacity ensembles.  _arXiv preprint arXiv:2011.03856_.
  * Cohen (1996) Paul R Cohen. 1996.  Empirical methods for artificial intelligence.  _IEEE Intelligent Systems_.
  * Dai and Adel (2020) Xiang Dai and Heike Adel. 2020.  An analysis of simple data augmentation for named entity recognition.  In _Proceedings of the 28th International Conference on Computational Linguistics_ , pages 3861–3867.
  * Dasgupta et al. (2018) Ishita Dasgupta, Demi Guo, Andreas Stuhlmüller, Samuel J Gershman, and Noah D Goodman. 2018.  Evaluating compositionality in sentence embeddings.  _arXiv preprint arXiv:1802.04302_.
  * Dayanik and Padó (2020) Erenay Dayanik and Sebastian Padó. 2020.  Masking actor information leads to fairer political claims detection.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 4385–4391.
  * Delobelle et al. (2020) Pieter Delobelle, Thomas Winters, and Bettina Berendt. 2020.  Robbert: a dutch roberta-based language model.  _arXiv preprint arXiv:2001.06286_.
  * Devlin et al. (2019) Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2019.  BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.  In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_ , pages 4171–4186.
  * Ding et al. (2020) Bosheng Ding, Linlin Liu, Lidong Bing, Canasai Kruengkrai, Thien Hai Nguyen, Shafiq Joty, Luo Si, and Chunyan Miao. 2020.  Daga: Data augmentation with a generation approach for low-resource tagging tasks.  _arXiv preprint arXiv:2011.01549_.
  * Durrett and Klein (2014) Greg Durrett and Dan Klein. 2014.  A Joint Model for Entity Analysis: Coreference, Typing, and Linking.  _Transactions of the Association for Computational Linguistics_ , 2:477–490.
  * Ebrahimi et al. (2018) Javid Ebrahimi, Anyi Rao, Daniel Lowd, and Dejing Dou. 2018.  Hotflip: White-box adversarial examples for text classification.  In _Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_ , pages 31–36.
  * Eiselen (2016) Roald Eiselen. 2016.  Government domain named entity recognition for south african languages.  In _Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)_ , pages 3344–3348.
  * Fawzi et al. (2016) Alhussein Fawzi, Seyed-Mohsen Moosavi-Dezfooli, and Pascal Frossard. 2016.  Robustness of classifiers: from adversarial to random noise.  In _Proceedings of the 30th International Conference on Neural Information Processing Systems_ , pages 1632–1640.
  * Geva et al. (2019) Mor Geva, Yoav Goldberg, and Jonathan Berant. 2019.  Are we modeling the task or the annotator? an investigation of annotator bias in natural language understanding datasets.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 1161–1166.
  * Ghaddar and Langlais (2018) Abbas Ghaddar and Philippe Langlais. 2018.  Transforming Wikipedia into a Large-Scale Fine-Grained Entity Type Corpus.  In _Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)_.
  * Ghaddar and Langlais (2016) Abbas Ghaddar and Phillippe Langlais. 2016.  Coreference in Wikipedia: Main concept resolution.  In _Proceedings of The 20th SIGNLL Conference on Computational Natural Language Learning_ , pages 229–238.
  * Ghaddar and Langlais (2017) Abbas Ghaddar and Phillippe Langlais. 2017.  Winer: A wikipedia annotated corpus for named entity recognition.  In _Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers)_ , pages 413–422.
  * Goodfellow et al. (2014) Ian J Goodfellow, Jonathon Shlens, and Christian Szegedy. 2014.  Explaining and harnessing adversarial examples.  _arXiv preprint arXiv:1412.6572_.
  * Gururangan et al. (2018) Suchin Gururangan, Swabha Swayamdipta, Omer Levy, Roy Schwartz, Samuel Bowman, and Noah A Smith. 2018.  Annotation artifacts in natural language inference data.  In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)_ , pages 107–112.
  * He et al. (2019) He He, Sheng Zha, and Haohan Wang. 2019.  Unlearn dataset bias in natural language inference by fitting the residual.  _EMNLP-IJCNLP 2019_ , page 132.
  * Heafield (2011) Kenneth Heafield. 2011.  KenLM: faster and smaller language model queries.  In _Proceedings of the EMNLP 2011 Sixth Workshop on Statistical Machine Translation_ , pages 187–197, Edinburgh, Scotland, United Kingdom.
  * Hendrycks and Gimpel (2017) Dan Hendrycks and Kevin Gimpel. 2017.  A baseline for detecting misclassified and out-of-distribution examples in neural networks.  _Proceedings of International Conference on Learning Representations_.
  * Hendrycks et al. (2020) Dan Hendrycks, Xiaoyuan Liu, Eric Wallace, Adam Dziedzic, Rishabh Krishnan, and Dawn Song. 2020.  Pretrained transformers improve out-of-distribution robustness.  _arXiv preprint arXiv:2004.06100_.
  * Hochreiter and Schmidhuber (1997) Sepp Hochreiter and Jürgen Schmidhuber. 1997.  Long short-term memory.  _Neural computation_ , 9(8):1735–1780.
  * Hvingelby et al. (2020) Rasmus Hvingelby, Amalie Brogaard Pauli, Maria Barrett, Christina Rosted, Lasse Malm Lidegaard, and Anders Søgaard. 2020.  Dane: A named entity resource for danish.  In _Proceedings of the 12th Language Resources and Evaluation Conference_ , pages 4597–4604.
  * Jawahar et al. (2019) Ganesh Jawahar, Benoît Sagot, and Djamé Seddah. 2019.  What Does BERT Learn about the Structure of Language?  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 3651–3657.
  * Joshi et al. (2020) Mandar Joshi, Danqi Chen, Yinhan Liu, Daniel S Weld, Luke Zettlemoyer, and Omer Levy. 2020.  Spanbert: Improving pre-training by representing and predicting spans.  _Transactions of the Association for Computational Linguistics_ , 8:64–77.
  * Le et al. (2020) Hang Le, Loïc Vial, Jibril Frej, Vincent Segonne, Maximin Coavoux, Benjamin Lecouteux, Alexandre Allauzen, Benoit Crabbé, Laurent Besacier, and Didier Schwab. 2020.  Flaubert: Unsupervised language model pre-training for french.  In _Proceedings of The 12th Language Resources and Evaluation Conference_ , pages 2479–2490.
  * Le Bras et al. (2020) Ronan Le Bras, Swabha Swayamdipta, Chandra Bhagavatula, Rowan Zellers, Matthew Peters, Ashish Sabharwal, and Yejin Choi. 2020.  Adversarial filters of dataset biases.  In _International Conference on Machine Learning_ , pages 1078–1088. PMLR.
  * Li et al. (2020) Xiaoya Li, Jingrong Feng, Yuxian Meng, Qinghong Han, Fei Wu, and Jiwei Li. 2020\.  A unified MRC framework for named entity recognition.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 5849–5859.
  * Lin et al. (2020) Hongyu Lin, Yaojie Lu, Jialong Tang, Xianpei Han, Le Sun, Zhicheng Wei, and Nicholas Jing Yuan. 2020.  A rigorous study on named entity recognition: Can fine-tuning pretrained model lead to the promised land?  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 7291–7300.
  * Liu et al. (2019) Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019.  Roberta: A robustly optimized bert pretraining approach.  _arXiv preprint arXiv:1907.11692_.
  * Ljubešić et al. (2018) Nikola Ljubešić, Željko Agić, Filip Klubička, Vuk Batanović, and Tomaž Erjavec. 2018.  Training corpus hr500k 1.0.  Slovenian language resource repository CLARIN.SI.
  * Luoma et al. (2020) Jouni Luoma, Miika Oinonen, Maria Pyykönen, Veronika Laippala, and Sampo Pyysalo. 2020.  A broad-coverage corpus for finnish named entity recognition.  In _Proceedings of The 12th Language Resources and Evaluation Conference_ , pages 4615–4624.
  * Mahabadi et al. (2020) Rabeeh Karimi Mahabadi, Yonatan Belinkov, and James Henderson. 2020.  End-to-end bias mitigation by modelling biases in corpora.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 8706–8716. Association for Computational Linguistics.
  * Manning et al. (2014) Christopher D. Manning, Mihai Surdeanu, John Bauer, Jenny Rose Finkel, Steven Bethard, and David McClosky. 2014.  The Stanford CoreNLP Natural Language Processing Toolkit.  In _ACL (System Demonstrations)_ , pages 55–60.
  * Martin et al. (2019) Louis Martin, Benjamin Muller, Pedro Javier Ortiz Suárez, Yoann Dupont, Laurent Romary, Éric Villemonte de la Clergerie, Djamé Seddah, and Benoît Sagot. 2019.  Camembert: a tasty french language model.  _arXiv preprint arXiv:1911.03894_.
  * Mayhew et al. (2020) Stephen Mayhew, Gupta Nitish, and Dan Roth. 2020.  Robust named entity recognition with truecasing pretraining.  In _Proceedings of the AAAI Conference on Artificial Intelligence_ , pages 8480–8487.
  * Mayhew et al. (2019) Stephen Mayhew, Tatiana Tsygankova, and Dan Roth. 2019.  ner and pos when nothing is capitalized.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 6257–6262.
  * McCoy et al. (2019) Tom McCoy, Ellie Pavlick, and Tal Linzen. 2019.  Right for the Wrong Reasons: Diagnosing Syntactic Heuristics in Natural Language Inference.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 3428–3448.
  * Min et al. (2020) Junghyun Min, R Thomas McCoy, Dipanjan Das, Emily Pitler, and Tal Linzen. 2020.  Syntactic data augmentation increases robustness to inference heuristics.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 2339–2352.
  * Miyato et al. (2016) Takeru Miyato, Andrew M Dai, and Ian Goodfellow. 2016.  Adversarial training methods for semi-supervised text classification.  _arXiv preprint arXiv:1605.07725_.
  * Moosavi et al. (2020) Nafise Sadat Moosavi, Marcel de Boer, Prasetya Ajie Utama, and Iryna Gurevych. 2020\.  Improving robustness by augmenting training sentences with predicate-argument structures.  _arXiv preprint arXiv:2010.12510_.
  * Nguyen et al. (2020) Thong Nguyen, Duy Nguyen, and Pramod Rao. 2020.  Adaptive Name Entity Recognition under Highly Unbalanced Data.  _arXiv preprint arXiv:2003.10296_.
  * Nivre et al. (2016) Joakim Nivre, Marie-Catherine De Marneffe, Filip Ginter, Yoav Goldberg, Jan Hajic, Christopher D Manning, Ryan McDonald, Slav Petrov, Sampo Pyysalo, Natalia Silveira, et al. 2016.  Universal dependencies v1: A multilingual treebank collection.  In _Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC’16)_ , pages 1659–1666.
  * Padó et al. (2019) Sebastian Padó, André Blessing, Nico Blokker, Erenay Dayanik, Sebastian Haunss, and Jonas Kuhn. 2019.  Who sides with whom? towards computational construction of discourse networks for political debates.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 2841–2847.
  * Pan et al. (2017) Xiaoman Pan, Boliang Zhang, Jonathan May, Joel Nothman, Kevin Knight, and Heng Ji. 2017.  Cross-lingual name tagging and linking for 282 languages.  In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)_ , pages 1946–1958.
  * Papineni et al. (2002) Kishore Papineni, Salim Roukos, Todd Ward, and Wei-Jing Zhu. 2002.  Bleu: a method for automatic evaluation of machine translation.  In _Proceedings of the 40th annual meeting of the Association for Computational Linguistics_ , pages 311–318.
  * Peters et al. (2018) Matthew Peters, Mark Neumann, Mohit Iyyer, Matt Gardner, Christopher Clark, Kenton Lee, and Luke Zettlemoyer. 2018.  Deep Contextualized Word Representations.  In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_ , pages 2227–2237.
  * Poliak et al. (2018) Adam Poliak, Jason Naradowsky, Aparajita Haldar, Rachel Rudinger, and Benjamin Van Durme. 2018.  Hypothesis only baselines in natural language inference.  In _Proceedings of the Seventh Joint Conference on Lexical and Computational Semantics_ , pages 180–191.
  * Pradhan et al. (2012) Sameer Pradhan, Alessandro Moschitti, Nianwen Xue, Olga Uryupina, and Yuchen Zhang. 2012.  CoNLL-2012 shared task: Modeling multilingual unrestricted coreference in OntoNotes.  In _Joint Conference on EMNLP and CoNLL-Shared Task_ , pages 1–40.
  * Ratinov and Roth (2009) Lev Ratinov and Dan Roth. 2009.  Design challenges and misconceptions in named entity recognition.  In _Proceedings of the Thirteenth Conference on Computational Natural Language Learning_ , pages 147–155. Association for Computational Linguistics.
  * Recht et al. (2019) Benjamin Recht, Rebecca Roelofs, Ludwig Schmidt, and Vaishaal Shankar. 2019.  Do imagenet classifiers generalize to imagenet?  In _International Conference on Machine Learning_ , pages 5389–5400. PMLR.
  * Sanh et al. (2020) Victor Sanh, Thomas Wolf, Yonatan Belinkov, and Alexander M Rush. 2020.  Learning from others’ mistakes: Avoiding dataset biases without modeling them.  _arXiv preprint arXiv:2012.01300_.
  * Schuster et al. (2019) Tal Schuster, Darsh Shah, Yun Jie Serene Yeo, Daniel Roberto Filizzola Ortiz, Enrico Santus, and Regina Barzilay. 2019.  Towards debiasing fact verification models.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 3410–3416.
  * Seltzer et al. (2013) Michael L Seltzer, Dong Yu, and Yongqiang Wang. 2013.  An investigation of deep neural networks for noise robust speech recognition.  In _2013 IEEE international conference on acoustics, speech and signal processing_ , pages 7398–7402. IEEE.
  * Shah et al. (2020) Deven Santosh Shah, H Andrew Schwartz, and Dirk Hovy. 2020.  Predictive biases in natural language processing models: A conceptual framework and overview.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 5248–5264.
  * Søgaard (2013) Anders Søgaard. 2013.  Part-of-speech tagging with antagonistic adversaries.  In _Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_ , pages 640–644.
  * Strauss et al. (2016) Benjamin Strauss, Bethany Toma, Alan Ritter, Marie-Catherine de Marneffe, and Wei Xu. 2016.  Results of the wnut16 named entity recognition shared task.  In _Proceedings of the 2nd Workshop on Noisy User-generated Text (WNUT)_ , pages 138–144.
  * Szegedy et al. (2013) Christian Szegedy, Wojciech Zaremba, Ilya Sutskever, Joan Bruna, Dumitru Erhan, Ian Goodfellow, and Rob Fergus. 2013.  Intriguing properties of neural networks.  _arXiv preprint arXiv:1312.6199_.
  * Thorne et al. (2018) James Thorne, Andreas Vlachos, Christos Christodoulopoulos, and Arpit Mittal. 2018\.  Fever: a large-scale dataset for fact extraction and verification.  In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)_ , pages 809–819.
  * Tjong Kim Sang (2002) Erik F. Tjong Kim Sang. 2002.  Introduction to the CoNLL-2002 shared task: Language-independent named entity recognition.  In _COLING-02: The 6th Conference on Natural Language Learning 2002 (CoNLL-2002)_.
  * Tjong Kim Sang and De Meulder (2003) Erik F Tjong Kim Sang and Fien De Meulder. 2003.  Introduction to the CoNLL-2003 shared task: Language-independent named entity recognition.  In _Proceedings of the seventh conference on Natural language learning at HLT-NAACL 2003-Volume 4_ , pages 142–147. Association for Computational Linguistics.
  * Ulčar and Robnik-Šikonja (2020) Matej Ulčar and Marko Robnik-Šikonja. 2020.  Finest bert and crosloengual bert: less is more in multilingual models.  _arXiv preprint arXiv:2006.07890_.
  * Utama et al. (2020a) Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020a.  Mind the trade-off: Debiasing nlu models without degrading the in-distribution performance.  _arXiv preprint arXiv:2005.00315_.
  * Utama et al. (2020b) Prasetya Ajie Utama, Nafise Sadat Moosavi, and Iryna Gurevych. 2020b.  Towards debiasing nlu models from unknown biases.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 7597–7610.
  * Vaswani et al. (2017) Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Łukasz Kaiser, and Illia Polosukhin. 2017.  Attention is all you need.  In _Advances in Neural Information Processing Systems_ , pages 5998–6008.
  * Virtanen et al. (2019) Antti Virtanen, Jenna Kanerva, Rami Ilo, Jouni Luoma, Juhani Luotolahti, Tapio Salakoski, Filip Ginter, and Sampo Pyysalo. 2019.  Multilingual is not enough: Bert for finnish.  _arXiv preprint arXiv:1912.07076_.
  * de Vries et al. (2019) Wietse de Vries, Andreas van Cranenburgh, Arianna Bisazza, Tommaso Caselli, Gertjan van Noord, and Malvina Nissim. 2019.  Bertje: A dutch bert model.  _arXiv preprint arXiv:1912.09582_.
  * Wang et al. (2018) Alex Wang, Amanpreet Singh, Julian Michael, Felix Hill, Omer Levy, and Samuel Bowman. 2018.  Glue: A multi-task benchmark and analysis platform for natural language understanding.  In _Proceedings of the 2018 EMNLP Workshop BlackboxNLP: Analyzing and Interpreting Neural Networks for NLP_ , pages 353–355.
  * Williams et al. (2017) Adina Williams, Nikita Nangia, and Samuel R Bowman. 2017.  A broad-coverage challenge corpus for sentence understanding through inference.  _arXiv preprint arXiv:1704.05426_.
  * Xin et al. (2020) Ji Xin, Raphael Tang, Jaejun Lee, Yaoliang Yu, and Jimmy Lin. 2020.  DeeBERT: Dynamic early exiting for accelerating BERT inference.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_ , pages 2246–2251, Online. Association for Computational Linguistics.
  * Yaghoobzadeh et al. (2019) Yadollah Yaghoobzadeh, Remi Tachet, Timothy J Hazen, and Alessandro Sordoni. 2019\.  Robust natural language inference models with example forgetting.  _arXiv preprint arXiv:1911.03861_.
  * Yu et al. (2020) Juntao Yu, Bernd Bohnet, and Massimo Poesio. 2020.  Named entity recognition as dependency parsing.  _arXiv preprint arXiv:2005.07150_.
  * Zellers et al. (2018) Rowan Zellers, Yonatan Bisk, Roy Schwartz, and Yejin Choi. 2018.  SWAG: A Large-Scale Adversarial Dataset for Grounded Commonsense Inference.  In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , pages 93–104.
  * Zeng et al. (2020) Xiangji Zeng, Yunliang Li, Yuchen Zhai, and Yin Zhang. 2020.  Counterfactual generator: A weakly-supervised method for named entity recognition.  In _Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)_ , pages 7270–7280.
  * Zhang et al. (2019) Yuan Zhang, Jason Baldridge, and Luheng He. 2019.  Paws: Paraphrase adversaries from word scrambling.  In _Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)_ , pages 1298–1308.
  * Zheng et al. (2019) Changmeng Zheng, Yi Cai, Jingyun Xu, Ho-fung Leung, and Guandong Xu. 2019.  A Boundary-aware Neural Model for Nested Named Entity Recognition.  In _Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)_ , pages 357–366.
  * Zhou et al. (2019) Joey Tianyi Zhou, Hao Zhang, Di Jin, Hongyuan Zhu, Meng Fang, Rick Siow Mong Goh, and Kenneth Kwok. 2019.  Dual adversarial neural transfer for low-resource named entity recognition.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 3461–3471.
  * Zhu et al. (2019) Chen Zhu, Yu Cheng, Zhe Gan, Siqi Sun, Thomas Goldstein, and Jingjing Liu. 2019\.  Freelb: Enhanced adversarial training for language understanding.  _arXiv preprint arXiv:1909.11764_.