# He Thinks He Knows Better than the Doctors:
BERT for Event Factuality Fails on Pragmatics

Nanjiang Jiang
Department of Linguistics
The Ohio State University
<EMAIL_ADDRESS>
&Marie-Catherine de Marneffe
Department of Linguistics
The Ohio State University
<EMAIL_ADDRESS>

###### Abstract

We investigate how well BERT performs on predicting factuality in several
existing English datasets, encompassing various linguistic constructions.
Although BERT obtains a strong performance on most datasets, it does so by
exploiting common surface patterns that correlate with certain factuality
labels, and it fails on instances where pragmatic reasoning is necessary.
Contrary to what the high performance suggests, we are still far from having a
robust system for factuality prediction.

##  1 Introduction

MegaVeridicality |  Someone was misinformed that something happened${}^{\text{-2.7}}$ .
---|---
CB |  Hazel had not felt so much bewildered since Blackberry had talked about the raft beside the Enborne. Obviously, the stones could not possibly be anything to do with El-ahrairah. It seemed to him that Strawberry might as well have said that his tail was${}^{\text{-1.33}}$ an oak tree.
RP |  The man managed to stay${}^{\text{3}}$ on his horse. / The man did not manage to stay${}^{\text{-2.5}}$ on his horse.
FactBank |  Helicopters are flying${}^{\text{3.0}}$ over northern New York today trying${}^{\text{3.0}}$ to locate${}^{\text{0}}$ people stranded${}^{\text{3.0}}$ without food, heat or medicine.
MEANTIME |  Alongside both announcements${}^{\text{3.0}}$ , Jobs also announced${}^{\text{3.0}}$ a new iCloud service to sync${}^{\text{0}}$ data among all devices.
UW |  Those plates may have come${}^{\text{1.4}}$ from a machine shop in north Carolina, where a friend of Rudolph worked${}^{\text{3.0}}$ .
UDS-IH2 |  DPA: Iraqi authorities announced${}^{\text{2.25}}$ that they had busted${}^{\text{2.625}}$ up 3 terrorist cells operating${}^{\text{2.625}}$ in Baghdad.

Table 1: Example items from each dataset. The annotated event predicates are
underlined with their factuality annotations in superscript. For the datasets
focusing on embedded events (first group), the clause-embedding verbs are in
bold and the entailment-canceling environments (if any) are slanted.

Predicting event factuality111The terms veridicality and speaker commitment
refer to the same underlying linguistic phenomenon. is the task of identifying
to what extent an event mentioned in a sentence is presented by the author as
factual. It is a complex semantic and pragmatic phenomenon: in John thinks he
knows better than the doctors, we infer that John probably doesn’t know better
than the doctors. Event factuality inference is prevalent in human
communication and matters for tasks which depend on natural language
understanding, such as information extraction. For instance, in the FactBank
example (Saurí and Pustejovsky, 2009) in Table 1, an information extraction
system should extract people are stranded without food but not helicopters
located people stranded without food.

The current state-of-the-art model for factuality prediction on English is
Pouran Ben Veyseh et al. (2019), obtaining the best performance on four
factuality datasets: FactBank, MEANTIME (Minard et al., 2016), UW Lee et al.
(2015) and UDS-IH2 Rudinger et al. (2018). Traditionally, event factuality is
thought to be triggered by fixed properties of lexical items. The Rule-based
model of Stanovsky et al. (2017) took such an approach: they used lexical
rules and dependency trees to determine whether an event in a sentence is
factual, based on the properties of the lexical items that embed the event in
question. Rudinger et al. (2018) proposed the first end-to-end model for
factuality with LSTMs. Pouran Ben Veyseh et al. (2019) used BERT
representations with a graph convolutional network and obtained a large
improvement over Rudinger et al. (2018) and over Stanovsky et al. (2017)’s
Rule-based model (except for one metric on the UW dataset).

However, it is not clear what these end-to-end models learn and what features
are encoded in their representations. In particular, they do not seem capable
of generalizing to events embedded under certain linguistic constructions.
White et al. (2018) showed that the Rudinger et al. (2018)’s models exhibit
systematic errors on MegaVeridicality, which contains factuality inferences
purely triggered by the semantics of clause-embedding verbs in specific
syntactic contexts. Jiang and de Marneffe (2019a) showed that Stanovsky et
al.’s and Rudinger et al.’s models fail to perform well on the CommitmentBank
(de Marneffe et al., 2019) which contains events under clause-embedding verbs
in an entailment-canceling environment (negation, question, modal or
antecedent of conditional).

In this paper, we investigate how well BERT, using a standard fine-tuning
approach,222We only augment BERT with a task-specific layer, instead of
proposing a new task-specific model as in Pouran Ben Veyseh et al. (2019).
performs on seven factuality datasets, including those focusing on embedded
events which have been shown to be challenging (White et al. 2018 and Jiang
and de Marneffe 2019a). The application of BERT to datasets focusing on
embedded events has been limited to the setup of natural language inference
(NLI) Poliak et al. (2018); Jiang and de Marneffe (2019b); Ross and Pavlick
(2019). In the NLI setup, an item is a premise-hypothesis pair, with a
categorical label for whether the event described in the hypothesis can be
inferred by the premise. The categorical labels are obtained by discretizing
the original real-valued annotations. For example, given the premise the man
managed to stay on his horse (RP example in Table 1) and the hypothesis the
man stayed on his horse, a model should predict that the hypothesis can be
inferred from the premise. In the factuality setup, an item contains a
sentence with one or more spans corresponding to events, with real-valued
annotations for the factuality of the event. By adopting the event factuality
setup, we study whether models can predict not only the polarity but also the
gradience in factuality judgments (which is removed in the NLI-style
discretized labels). Here, we provide an in-depth analysis to understand which
kind of items BERT fares well on, and which kind it fails on. Our analysis
shows that, while BERT can pick up on subtle surface patterns, it consistently
fails on items where the surface patterns do not lead to the factuality labels
frequently associated with the pattern, and for which pragmatic reasoning is
necessary.

##  2 Event factuality datasets

Several event factuality datasets for English have been introduced, with
examples from each shown in Table 1. These datasets differ with respect to
some of the features that affect event factuality.

#### Embedded events

The datasets differ with respect to which events are annotated for factuality.
The first category, including MegaVeridicality (White et al., 2018),
CommitmentBank (CB), and Ross and Pavlick (2019) (RP), only contains sentences
with clause-embedding verbs and factuality is annotated solely for the event
described by the embedded clause. These datasets were used to study speaker
commitment towards the embedded content, evaluating theories of lexical
semantics (i.a., Kiparsky and Kiparsky, 1970; Karttunen, 1971a; Beaver, 2010),
and probing whether neural model representations contain lexical semantic
information. In the datasets of the second category (FactBank, MEANTIME, UW
and UDS-IH2), events in both main clauses and embedded clauses (if any) are
annotated. For instance, the example for UDS-IH2 in Table 1 has annotations
for the main clause event announced and the embedded clause event busted,
while the example for RP is annotated only for the embedded clause event stay,
but not for the main clause event managed.

#### Genres

The datasets also differ in genre: FactBank, MEANTIME and UW are newswire
data. Since newswire sentences tend to describe factual events, these datasets
have annotations biased towards factual. UDS-IH2, an extension of White et al.
(2016), comes from the English Web Treebank (Bies et al., 2012) containing
weblogs, emails, and other web text. CB comes from three genres: newswire
(Wall Street Journal), fiction (British National Corpus), and dialog
(Switchboard). RP contains short sentences sampled from MultiNLI (Williams et
al., 2018) from 10 different genres. MegaVeridicality contains artificially
constructed “semantically bleached” sentences to remove confound of pragmatics
and world-knowledge, and to collect baseline judgments of how much the verb by
itself affects the factuality of the content of its complement in certain
syntactic constructions.

#### Entailment-canceling environments

The three datasets in the first category differ with respect to whether the
clause-embedding verbs are under some entailment-canceling environment, such
as negation. Under the framework of implicative signatures (Karttunen, 1971a;
Nairn et al., 2006; Karttunen, 2012), a clause-embedding verb (in a certain
syntactic frame, details later) has a lexical semantics (a signature)
indicating whether the content of its complement is factual (+), nonfactual
(-), or neutral (o, no indication of whether the event is factual or not). A
verb signature has the form X/Y, where X is the factuality of the content of
the clausal complement when the sentence has positive polarity (not embedded
under any entailment-canceling environment), and Y is the factuality when the
clause-embedding verb is under negation. In the RP example in Table 1, manage
to has signature +/- which, in the positive polarity sentence the man managed
to stay on his horse, predicts the embedded event stay to be factual (such
intuition is corroborated by the $+3$ human annotation). Conversely, in the
negative polarity sentence the man did not manage to stay on his horse, the -
signature signals that stay is nonfactual (again corroborated by the $-2.5$
human annotation). For manage to, negation cancels the factuality of its
embedded event.

While such a framework assumes that different entailment-canceling
environments (negation, modal, question, and antecedent of conditional) have
the same effects on the factuality of the content of the complement (Chierchia
and McConnell-Ginet, 1990), there is evidence for varying effects of
environments. Karttunen (1971b) points out that, while the content of
complement of verbs such as realize and discover stays factual under negation
(compare 2 and 2), it does not under a question 2 or in the antecedent of a
conditional 2.

. I realized that I had not told the truth.${}^{\texttt{+}}$

. I didn’t realize that I had not told the truth.${}^{\texttt{+}}$

. Did you realize that you had not told the truth${}^{\texttt{o}}$?

. If I realize later that I have not told the truth${}^{\texttt{o}}$, I will
confess it to everyone.

Smith and Hall (2014) provided experimental evidence that the content of the
complement of know is perceived as more factual when know is under negation
than when it is in the antecedent of a conditional.

In MegaVeridicality, each positive polarity sentence is paired with a negative
polarity sentence where the clause-embedding verb is negated. Similarly in RP,
for each naturally occurring sentence of positive polarity, a minimal pair
negative polarity sentence was automatically generated. The verbs in CB appear
in four entailment-canceling environments: negation, modal, question, and
antecedent of conditional.

#### Frames

Among the datasets in the first category, the clause-embedding verbs are under
different syntactic contexts/frames, which also affect the factuality of their
embedded events. For example, forget has signature +/+ in forget that S, but
-/+ in forget to VP. That is, in forget that S, the content of the clausal
complement S is factual in both someone forgot that S and someone didn’t
forget that S. In forget to VP, the content of the infinitival complement VP
is factual in someone didn’t forget to VP, but not in someone forgot to VP.

CB contains only VERB that S frames. RP contains both VERB that S and VERB to
VP frames. MegaVeridicality exhibits nine frames, consisting of four argument
structures and manipulations of active/passive voiceand eventive/stative
embedded VP: VERB that S, was VERBed that S, VERB for NP to VP, VERB NP to VP-
eventive, VERB NP to VP-stative, NP was VERBed to VP-eventive, NP was VERBed
to VP-stative, VERB to VP-eventive, VERB to VP-stative.

#### Annotation scales

The original FactBank and MEANTIME annotations are categorical values. We use
Stanovsky et al. (2017)’s unified representations for FactBank and MEANTIME,
which contain labels in the $[-3,3]$ range derived from to the original
categorical values in a rule-based manner. The original annotations of
MegaVeridicality contain three categorical values yes/maybe/no, which we
mapped to $3$/$0$/$-3$ respectively. We then take the mean of the annotations
for each item. The original annotations in RP are integers in $[-2,2]$. We
multiply each RP annotation by 1.5 to get labels in the same range as in the
other datasets. The mean of the converted annotations is taken as the gold
label for each item.

##  3 Linguistic approaches to factuality

Most work in NLP on event factuality has taken a lexicalist approach, tracing
back factuality to fixed properties of lexical items. Under such an approach,
properties of the lexical patterns present in the sentence determine the
factuality of the event, without taking into account contextual factors. We
will refer to the inference calculated from lexical patterns only as expected
inference. For instance, in , the expected inference for the event had
embedded under believe is neutral. Indeed, since both true and false things
can be believed, one should not infer from A believes that S that S is true
(in other words, believe has as o/o signature), making believe a so-called
“non-factive” verb by opposition to “factive” verbs (such as know or realize,
which generally entail the truth of their complements both in positive
polarity sentences 2 and in entailment-canceling environments 2, Kiparsky and
Kiparsky (1970)). However, lexical theories neglect the pragmatic enrichment
that is pervasive in human communication and fall short in predicting the
correct inference in , where people judged the content of the complement to be
true (as indicated by the annotation score of $2.38$).

. Annabel could hardly believe that she had${}^{\text{2.38}}$ a daughter about
to go to university.

In FactBank, Saurí and Pustejovsky (2009) took a lexicalist approach, seeking
to capture only the effect of lexical meaning and knowledge local to the
annotated sentence: annotators were linguistically trained and instructed to
avoid using knowledge from the world or from the surrounding context of the
sentence. However, it has been shown that such annotations do not always align
with judgments from linguistically-naive annotators. de Marneffe et al. (2012)
and Lee et al. (2015) re-annotated part of FactBank with crowdworkers who were
given minimal guidelines. They found that events embedded under report verbs
(e.g., say), annotated as neutral in FactBank (since, similarly to believe,
one can report both true and false things), are often annotated as factual by
crowdworkers. Ross and Pavlick (2019) showed that their annotations also
exhibit such a veridicality bias: events are often perceived as
factual/nonfactual, even when the expected inference specified by the
signature is neutral. The reason behind this misalignment is commonly
attributed to pragmatics: crowdworkers use various contextual features to
perform pragmatic reasoning that overrides the expected inference defined by
lexical semantics. There has been theoretical linguistics work arguing that
factuality is indeed tied to the discourse structure and not simply lexically
controlled (i.a., Simons et al. 2010).

Figure 1: Fitted probabilities of true expected inference category predicted
by the label of each item given by the ordered logistic regression model,
organized by the signature and polarity. Some examples of verb-frame with mean
probability less than 0.5 are labeled.

Further, our analysis of MegaVeridicality shows that there is also some
misalignment between the inference predicted by lexical semantics and the
human annotations, even in cases without pragmatic factors. Recall that
MegaVeridicality contains semantically bleached sentences where the only
semantically loaded word is the embedding verb. We used ordered logistic
regression to predict the expected inference category (+, o, -) specified by
the embedding verb signatures defined in Karttunen (2012) from the mean human
annotations.333The analysis is done on items with verb-frame combinations (and
their passive counterparts) for which Karttunen (2012) gives a signature
(i.e., 618 items from MegaVeridicality). The coefficient for mean human
annotations is 1.488 (with 0.097 standard error): thus, overall, the expected
inference aligns with the annotations.444The threshold for -|o is -2.165 with
SE 0.188. The threshold for o|+ is 0.429 with SE 0.144. However, there are
cases where they diverge. Figure 1 shows the fitted probability of the true
expected inference category for each item, organized by the signatures and
polarity. If the expected inference was always aligning with the human
judgments, the fitted probabilities would be close to 1 for all points.
However, many points have low fitted probabilities, especially when the
expected inference is o (e.g., negative polarity of +/o and -/o, positive
polarity of o/+ and o/-), showing that there is veridicality bias in
MegaVeridicality, similar to RP. Table 2 gives concrete examples from
MegaVeridicality and RP, for which the annotations often differ from the verb
signatures: events under not refuse to are systematically annotated as
factual, instead of the expected neutral. The RP examples contain minimal
content information (but the mismatch in these examples may involve pragmatic
reasoning).

not continue to signature: +/+, expected: +, observed: -
---
A particular person didn’t continue to do${}^{\text{-0.33}}$ a particular
thing.
A particular person didn’t continue to have${}^{\text{-1.5}}$ a particular
thing.
They did not continue to sit${}^{\text{-3}}$ in silence.
He did not continue to talk${}^{\text{-3}}$ about fish.
not pretend to signature: -/-, expected: -, observed: closer to o
Someone didn’t pretend to have${}^{\text{-1.2}}$ a particular thing.
He did not pretend to aim${}^{\text{-0.5}}$ at the girls.
{add/warn} that signature: o/+, expected: o, observed: +
Someone added that a particular thing happened${}^{\text{2.1}}$ .
Linda Degutis added that interventions have${}^{\text{2.5}}$ to be monitored.
Someone warned that a particular thing happened${}^{\text{2.1}}$ .
It warns that Mayor Giuliani ’s proposed pay freeze could destroy the NYPD ’s
new esprit de corps${}^{\text{2.5}}$ .
not {decline/refuse} to signature: -/o, expected: o, observed: +
A particular person didn’t decline to do${}^{\text{1.5}}$ a particular thing.
We do not decline to sanction${}^{\text{2.5}}$ such a result.
A particular person didn’t refuse to do${}^{\text{2.1}}$ a particular thing.
The commission did not refuse to interpret${}^{\text{2.0}}$ it.

Table 2: Items with verbs that often behave differently from the signatures.
The semantically-bleached sentences are from MegaVeridicality, the others from
RP. Gold labels are superscripted.

In any case, given that neural networks are function approximators, we
hypothesize that BERT can learn these surface-level lexical patterns in the
training data. But items where pragmatic reasoning overrides the lexical
patterns would probably be challenging for the model.

##  4 Model and experiment setup

To analyze what BERT can learn, we use the seven factuality datasets in Table
1.

#### Data preprocessing

| train | dev | test
---|---|---|---
MegaVeridicality | 2,200 | 626 | 2,200
CommitmentBank | 250 | 56 | 250
RP | 1,100 | 308 | 1,100
FactBank | 6,636 | 2,462 | 663
MEANTIME | 1,012 | 195 | 188
UW | 9,422 | 3,358 | 864
UDS-IH2 | 22,108 | 2,642 | 2,539

Table 3:  Number of events in each dataset split.

The annotations of CB and RP have been collected by asking annotators to rate
the factuality of the content of the complement, which may contain other
polarity and modality operators, whereas in FactBank annotators rated the
factuality of the normalized complement, without polarity and modality
operators. For example, the complement anything should be done in the short
term contains the modal operator should, while the normalized complement would
be anything is done in the short term. In MEANTIME, UW and UDS-IH2, annotators
rated the factuality of the event represented by a word in the original
sentence, which has the effect of removing such operators. Therefore, to
ensure a uniform interpretation of annotations between datasets, we semi-
automatically identified items in CB and RP where the complement is not
normalized,555We automatically identified whether the complement contains a
neg dependency relation, modal operators (should, could, can, must, perhaps,
might, maybe, may, shall, have to, would), or adverbs, and manually verified
the output. for which we take the whole embedded clause to be the span for
factuality prediction. Otherwise, we take the root of the embedded clause as
the span.

We also excluded 236 items in RP where the event for which annotations were
gathered cannot be represented by a single span from the sentence. For
example, for The Post Office is forbidden from ever attempting to close any
office, annotators were asked to rate the factuality of the Post Office is
forbidden from ever closing any office. Simply taking the span close any
office corresponds to the event of the Post Office close any office, but not
to the event for which annotations are collected.

#### Excluding data with low agreement annotation

There are items in RP and CB which exhibit bimodal annotations. For instance,
the sentence in RP White ethnics have ceased to be the dominant force in urban
life received 3 annotation scores: $-3$/nonfactual, $1.5$/between neutral and
factual, and $3$/factual. By taking the mean of such bimodal annotations, we
end up with a label of $0.5$/neutral, which is not representative of the
judgments in the individual annotations. For RP (where each item received
three annotations), we excluded 250 items where at least two annotations have
different signs. For CB (where each item received at least 8 annotations), we
follow Jiang and de Marneffe (2019a) by binning the responses into $[-3,-1]$,
$[0]$, $[1,3]$ and discarding items if less than 80% of the annotations fall
in the same bin.

#### Data splits

We used the standard train/dev/test split for FactBank, MEANTIME, UW, and UDS-
IH2. As indicated above, we only use the high agreement subset of CB with 556
items, with splits from Jiang and de Marneffe (2019b). We randomly split
MegaVeridicality and RP with stratified sampling to keep the distributions of
the clause-embedding verbs similar in each split. Table 3 gives the number of
items in each split.

#### Model architecture

The task is to predict a scalar value in $[-3,3]$ for each event described by
a span in the input sentence. A sentence is fed into BERT and the final-layer
representations for the event span are extracted. Since the spans have
variable lengths, the SelfAttentiveSpanExtractor (Gardner et al., 2018) is
used to weightedly combine the representations of multiple tokens and create a
single vector for the original event span. The extracted span vectors are fed
into a two-layer feed-forward network with tanh activation function to predict
a single scalar value. Our architecture is similar to Rudinger et al. (2018)’s
linear-biLSTM model, except that the input is encoded with BERT instead of
bidirectional LSTM, and a span extractor is used. The model is trained with
the smooth L1 loss.666The code and data are available at
https://github.com/njjiang/factuality_bert. The code is based on the toolkit
jiant v1 (Wang et al., 2019).

#### Evaluation metrics

Following previous work, we report mean absolute error (MAE), measuring
absolute fit, and Pearson’s $r$ correlation, measuring how well models capture
variability in the data. $r$ is considered more informative since some
datasets (MEANTIME in particular) are biased towards $+3$.

#### Model training

For all experiments, we fine-tuned BERT using the bert_large_cased model. Each
model is fine-tuned with at most 20 epochs, with a learning rate of $1e-5$.
Early stopping is used: training stops if the difference between Pearson’s $r$
and MAE does not increase for more than 5 epochs. Most training runs last more
than 10 epochs. The checkpoint with the highest difference between Pearson’s
$r$ and MAE on the dev set is used for testing. We explored several training
data combinations:
-Single: Train with each dataset individually;
-Shared: Treat all datasets as one;
-Multi: Datasets share the same BERT parameters while each has its own classifier parameters.

The Single and Shared setups may be combined with first fine-tuning BERT on
MultiNLI, denoted by the superscript M. We tested on the test set of the
respective datasets.

We also tested whether BERT improves on previous models on its ability to
generalize to embedded events. The models in Rudinger et al. (2018) were
trained on FactBank, MEANTIME, UW, and UDS-IH2 with shared encoder parameters
and separate classifier parameters, and an ensemble of the four classifiers.
To make a fair comparison, we followed Rudinger et al.’s setup by training
BERT on FactBank, MEANTIME, UW, and UDS-IH2 with one single set of
parameters777Unlike the Hybrid model of Rudinger et al. (2018), there is no
separate classifier parameters for each dataset. and tested on
MegaVeridicality and CommitmentBank.888For both datasets, examples from all
splits are used, following previous work.

| R |  Previous | MAE |  Previous
---|---|---|---|---
| Shared | SharedM | Single | SingleM | Multi |  SotA | Shared | SharedM | Single | SingleM | Multi |  SotA
CB | 0.865 | 0.869 | 0.831 | 0.878 | 0.89† |  | 0.713 | 0.722 | 0.777 | 0.648 | 0.617† |
RP | 0.806 | 0.813 | 0.867 | 0.867 | 0.87† |  | 0.733 | 0.714 | 0.621 | 0.619 | 0.608† |
MegaVeridicality | 0.876† | 0.873 | 0.857 | 0.863 | 0.857 |  | 0.508 | 0.501† | 0.531 | 0.523 | 0.533 |
FactBank | 0.836 | 0.845 | 0.914† | 0.901 | 0.903 |  0.903 | 0.42 | 0.417 | 0.228† | 0.241 | 0.236 |  0.31
MEANTIME | 0.557 | 0.572† | 0.503 | 0.513 | 0.491 |  0.702 | 0.333 | 0.338 | 0.355 | 0.345 | 0.319† |  0.204
UW | 0.776 | 0.787 | 0.868† | 0.868 | 0.865 |  0.83 | 0.532 | 0.523 | 0.349† | 0.351 | 0.351 |  0.42
UDS-IH2 | 0.845 | 0.843 | 0.855† | 0.854 | 0.853 |  0.909 | 0.794 | 0.804 | 0.76† | 0.763 | 0.766 |  0.726

Table 4: Performance on the test sets under different BERT training setups.
The best score obtained by our models for each dataset under each metric is
marked by $\dagger$. The overall best scores are highlighted. Each score is
the average from three runs with different random initialization. The previous
state-of-the-art results are given when available. All come from Pouran Ben
Veyseh et al. (2019), except the MAE score on UW which comes from Stanovsky et
al. (2017).

| MegaVeridicality | CB
---|---|---
| $r$ | MAE | $r$ | MAE
BERT | 0.60 | 1.09 | 0.59 | 1.40
Stanovsky et al. | - | - | 0.50 | 2.04
Rudinger et al. | 0.64 | - | 0.33 | 1.87

Table 5: Performance on MegaVeridicality and CommitmentBank across all splits
of the previous model (Stanovsky et al. 2017 and Rudinger et al. 2018) and
BERT trained on the concatenation of FactBank, MEANTIME, UW, UDS-IH2 using one
set of parameters. White et al. (2018) did not report MAE results for
MegaVeridicality.

##  5 Results

Table 4 shows performance on the various test sets with the different training
schemes. These models perform well and obtain the new state-of-the-art results
on FactBank and UW, and comparable performance to the previous models on the
other datasets (except for MEANTIME999The difference in performance for
MEANTIME might come from a difference in splitting: Pouran Ben Veyseh et al.
(2019)’s test set has a different size. Some of the gold labels in MEANTIME
also seem wrong.). Comparing Shared vs. SharedMand Single vs. SingleM, we see
that transferring with MNLI helps all datasets on at least one metric, except
for UDS-IH2 where MNLI-transfer hurts performance. The Multi and Single models
obtain the best performance on almost all datasets other than MegaVeridicality
and MEANTIME. The success of these models confirms the findings of Rudinger et
al. (2018) that having dataset-specific parameters is necessary for optimal
performance. Although this is expected, since each dataset has its own
specific features, the resulting model captures data-specific quirks rather
than generalizations about event factuality. This is problematic if one wants
to deploy the system in downstream applications, since which dataset the input
sentence will be more similar to is unknown a priori.

However, looking at whether BERT improves on the previous state-of-the-art
results for its ability to generalize to the linguistic constructions without
in-domain supervision, the results are less promising. Table 5 shows
performance of BERT trained on four factuality datasets and tested on
MegaVeridicality and CB across all splits, and the Rule-based and Hybrid
models’ performance reported in Jiang and de Marneffe (2019a) and White et al.
(2018). BERT improves on the other systems by only a small margin for CB, and
obtains no improvement for MegaVeridicality. Despite having a magnitude more
parameters and pretraining, BERT does not generalize to the embedded events
present in MegaVeridicality and CB. This shows that we are not achieving
robust natural language understanding, unlike what the near-human performance
on various NLU benchmarks suggests.

Finally, although RoBERTa Liu et al. (2019) has exhibited improvements over
BERT on many different tasks, we found that, in this case, using pretrained
RoBERTa instead of BERT does not yield much improvement. The predictions of
the two models are highly correlated, with 0.95 correlation over all datasets’
predictions.

##  6 Quantitative analysis: Expected inference

Here, we evaluate our hypothesis that BERT can learn subtle lexical patterns,
regardless of whether they align with lexical semantics theories, but
struggles when pragmatic reasoning overrides the lexical patterns. To do so,
we present results from a quantitative analysis using the notion of expected
inference. To facilitate meaningful analysis, we generated two random
train/dev/test splits of the same sizes as in Table 3 (besides the standard
split) for MegaVeridicality, CB, and RP. All items are present at least once
in the test sets. We trained the Multi model using three different random
initializations with each split.101010There is no model performing radically
better than the others. The Multi model achieves better results than the
Single one on CB and is getting comparable performance to the Single model on
the other datasets. We use the mean predictions of each item across all
initializations and all splits (unless stated otherwise).

###  6.1 Method

As described above, the expected inference of an item is the factuality label
predicted by lexical patterns only. We hypothesize that BERT does well on
items where the gold labels match the expected inference, and fails on those
that do not.

#### How to get the best expected inference?

To identify the expected inference, the approach varies by dataset. For the
datasets focusing on embedded events (MegaVeridicality, CB, and RP), we take,
as expected inference label, the mean labels of training items with the same
combination of features as the test item. Theoretically, the signatures should
capture the expected inference. However, as shown earlier, the signatures do
not always align with the observed annotations, and not all verbs have
signatures defined. The mean labels of training items with the same features
captures what the common patterns in the data are and what the model is
exposed to. In MegaVeridicality and RP, the features are clause-embedding
verb, polarity and frames. In CB, they are verb and entailment-canceling
environment.111111 The goal is to take items with the most matching features.
If there are no training items with the exact same combination of features, we
take items with the next best match, going down the list if the previous
features are not available:
\- MegaVeridicality and RP: verb-polarity, verb, polarity.
\- CB: verb, environment.

For FactBank, UW, and MEANTIME, the approach above does not apply because
these datasets contain matrix-clause and embedded events. We take the
predictions from Stanovsky et al.’s Rule-based
model121212https://github.com/gabrielStanovsky/unified-
factuality/tree/master/data/predictions_on_test as the expected inference,
since the Rule-based model uses lexical rules including the signatures. We
omitted UDS-IH2 from this analysis because there are no existing predictions
by the Rule-based model on UDS-IH2 available.

###  6.2 Results

We fitted a linear mixed effect model using the absolute error between the
expected inference and the label to predict the absolute error of the model
predictions, with random intercepts and slopes for each dataset. Results are
shown in Table 6. We see that the slopes are all positive, suggesting that the
error of the expected inference to the label is positively correlated with the
error of the model, as we hypothesized.

Dataset | $\alpha$ | $SE(\alpha)$ | $\beta$ | $SE(\beta)$
---|---|---|---|---
FactBank | -0.039 | 0.018 | 0.073 | 0.015
MEANTIME | -0.058 | 0.033 | 0.181 | 0.024
UW | 0.004 | 0.016 | 0.261 | 0.016
MegaVeridicality | 0.134 | 0.008 | 0.142 | 0.006
CB | 0.099 | 0.020 | 0.265 | 0.016
RP | 0.059 | 0.011 | 0.468 | 0.012

Table 6: Estimated random intercepts ($\alpha$) and slopes ($\beta$) for each
dataset and their standard errors. The fixed intercept is 0.228 with standard
error 0.033.

The slope for FactBank is much smaller than the slopes for the other datasets,
meaning that for FactBank, the error of the expected inference does not
predict the model’s errors as much as in the other datasets. This is due to
the fact that the errors in FactBank consist of items for which the lexicalist
and crowdsourced annotations may differ. The model, which has been trained on
crowdsourced datasets, makes predictions that are more in line with the
crowdsourced annotations but are errors compared to the lexicalist labels. For
example, 44% of the errors are reported events (e.g., X said that …) annotated
as neutral in FactBank (given that both true or false things can be reported)
but predicted as factual. Such reported events have been found to be annotated
as factual by crowdworkers (de Marneffe et al. 2012, Lee et al. 2015). On the
other hand, the expected inference (from the Rule-based model) also follows a
lexicalist approach. Therefore labels align well with the expected inference,
but the predictions do so poorly.

##  7 Qualitative analysis

The quantitative analysis shows that the model predictions are driven by
surface-level features. Not surprisingly, when a gold label of an item
diverges from the label of items with similar surface patterns, the model does
not do well. Here, we unpack which surface features are associated with
labels, and examine the edge cases in which surface features diverge from the
observed labels. We focus on the CB, RP, and MegaVeridicality datasets since
they focus on embedded events well studied in the literature.

Figure 2: Multi model’s predictions compared to gold labels for all CB items
present in all splits, by entailment-canceling environment. Diagonal line
shows perfect prediction.

###  7.1 CB

Figure 2 shows the scatterplot of the Multi model’s prediction vs. gold labels
on CB, divided by each entailment-canceling environment. As pointed out by
Jiang and de Marneffe (2019b), the interplay between the entailment-canceling
environment and the clause-embedding verb is often the deciding factor for the
factuality of the complement in CB. Items with factive embedding verbs tend
indeed to be judged as factual (most blue points in Figure 2 are at the top of
the panels). “Neg-raising” items contain negation in the matrix clause (not
{think/believe/know} $\phi$) but are interpreted as negating the content of
the complement clause ({think/believe/know} not $\phi$). Almost all items
involving a construction indicative of “Neg-raising” I don’t
think/believe/know $\phi$ have nonfactual labels (see × in first panel of
Figure 2). Items in modal environment are judged as factual (second panel
where most points are at the top).

In addition to the environment and the verb, there are more fine-grained
surface patterns predictive of human annotations. Polar question items with
nonfactive verbs often have near-0 factuality labels (third panel, orange
circles clustered in the middle). In tag-question items, the label of the
embedded event often matches the matrix clause polarity, such as 7.1 with a
matrix clause of positive polarity and a factual embedded event.

. […] I think it went${}^{\text{1.09}\ [\text{1.52}]}$ to Lockheed, didn’t it?

Following these statistical regularities, the model obtains good results by
correctly predicting the majority cases. However, it is less successful on
cases where the surface features do not lead to the usual label, and pragmatic
reasoning is required. The model predicts most of the neg-raising items
correctly, which make up 58% of the data under negation. But the neg-raising
pattern leads the model to predict negative values even when the labels are
positive, as in .131313We use the notation event span${}^{\text{label}\
[\text{prediction}]}$ throughout the rest of the paper.

. […] And I think society for such a long time said, well, you know, you’re
married, now you need to have your family and I don’t think it’s
been${}^{\text{1.25}\ [\text{-1.99}]}$ until recently that they had decided
that two people was a family.

It also wrongly predicts negative values for items where the context contains
a neg-raising-like substring (don’t think/believe), even when the targeted
event is embedded under another environment: question for , antecedent of
conditional for .

. B: All right, well. A: Um, short term, I don’t think anything’s going to be
done about it or probably should be done about it. B: Right. Uh, are you
saying you don’t think anything should be done in the short term${}^{\text{0}\
[\text{-1.73}]}$ ?

. […] I do not believe I am being unduly boastful if I say that very few ever
needed${}^{\text{2.3}\ [\text{-1.94}]}$ amendment.

Figure 3: Multi model’s predictions compared to gold labels for certain verbs
and frames in RP. Diagonal line shows perfect prediction.

###  7.2 RP

The surface features impacting the annotations in RP are the clause-embedding
verb, its syntactic frame, and polarity. Figure 3 shows the scatterplot of
label vs. prediction for items with certain verbs and frames, for which we
will show concrete examples later. The errors (circled points) in each panel
are often far away from the other points of the same polarity on the $y$-axis,
confirming the findings above that the model fails on items that diverge from
items with similar surface patterns. Generally, points are more widespread
along the $y$-axis than the $x$-axis, meaning that the model makes similar
predictions for items which share the same features, but it cannot account for
variability among such items. Indeed, the mean variance of the predictions for
items of each verb, frame, and polarity is 0.19, while the mean variance of
the gold labels for these items is 0.64.

Compare 7.2 and 7.2: they consist of the same verb convince with positive
polarity and they have similar predictions, but very different gold labels.
Most of the convince items of positive polarity are between neutral and
factual (between $0$ and $1.5$), such as 7.2. The model learned that from the
training data: all convince items of positive polarity have similar
predictions ranging from $0.7$ to $1.9$, with mean $1.05$ (also shown in the
first panel of Figure 3). However, 7.2 has a negative label of $-2$ unlike the
other convince items, because the following context I was mistaken clearly
states that the speaker’s belief is false, and therefore the event they would
fetch up at the house in Soho is not factual. Yet the model fails to take this
into account.

. I was convinced that the alarm was given when Mrs. Cavendish was in the
room${}^{\text{1.5}\ [\text{1.13}]}$ .

. I was convinced that they would fetch up at the house in Soho${}^{\text{-2}\
[\text{0.98}]}$ , but it appears I was mistaken.

###  7.3 MegaVeridicality

As shown in the expected inference analysis, MegaVeridicality exhibits the
same error pattern as CB and RP (failing on items where gold labels differ
from the ones of items sharing similar surface features). Unlike CB and RP,
MegaVeridicality is designed to rule out the effect of pragmatic reasoning.
Thus the errors for MegaVeridicality cannot be due to pragmatics. Where are
those stemming from? It is known that some verbs behave very differently in
different frames. However, the model was not exposed to the same combination
of verb and frame during training and testing, which leads to errors. For
example, mislead141414Other verbs with the same behavior and similar meaning
include dupe, deceive, fool. in the VERBed NP to VP frame in positive
polarity, as in 7.3, and its passive counterpart 7.3, suggests that the
embedded event is factual (someone did something), while in other
frame/polarity, the event is nonfactual, as in 7.3 and 7.3. The model,
following the patterns of mislead in other contexts, fails on 7.3 and 7.3
because the training set did not contain instances with mislead in a factual
context.

. Someone misled a particular person to do${}^{\text{2.7}\ [\text{-1.6}]}$ a
particular thing.

. A particular person was misled to do${}^{\text{2.7}\ [\text{-1.21}]}$ a
particular thing.

. Someone was misled that a particular thing happened${}^{\text{-1.5}\
[\text{-2.87}]}$ .

. Someone wasn’t misled to do${}^{\text{-0.3}\ [\text{-0.6}]}$ a particular
thing.

This shows that the model’s ability to reason is still limited to pattern
matching: it fails to induce how verb meaning interacts with syntactic frames
that are unseen during training. If we augment MegaVeridicality with more
items of verbs in these contexts (currently there is one example of each verb
under either polarity in most frames) and add them to the training set, BERT
would probably learn these behaviors.

Moreover, the model here exhibits a different pattern from White et al.
(2018), who found that their model cannot capture inferences whose polarity
mismatches the matrix clause polarity, as their model fails on items with
verbs that suggest nonfactuality of their complement such as fake, misinform
under positive polarity. As shown in the expected inference analysis in
section 6, our model is successful at these items, since it has memorized the
lexical pattern in the training data.

###  7.4 Error categorization

In this section, we study the kinds of reasoning that is needed to draw the
correct inference in items that the system does not handle correctly. For the
top 10% of the items sorted by absolute error in CB and RP, two linguistically
trained annotators annotated which factors lead to the observed factuality
inferences, according to factors put forth in the literature, as described
below.151515This is not an exhaustive list of reasoning types present in the
data, and having one of these properties is not sufficient for the model to
fail.

#### Prior probability of the event

Whether the event described is likely to be true is known to influence human
judgments of event factuality (Tonhauser et al., 2018; de Marneffe et al.,
2019). Events that are more likely to be factual a priori are often considered
as factual even when they are embedded, as in 7.4. Conversely, events that are
unlikely a priori are rated as nonfactual when embedded, as in 7.4.

. […] He took the scuffed leather document case off the seat beside him and
banged the door shut with the violence of someone who had not learned that car
doors do not need the same sort of treatment as those of railway
carriages${}^{\text{2.63}\ [\text{0.96}]}$

. In a column lampooning Pat Buchanan, Royko did not write that Mexico
was${}^{\text{-3}\ [\text{-0.3}]}$ a useless country that should be invaded
and turned over to Club Med.

#### Context suggests (non)factuality

The context may directly describe or give indirect cues about the factuality
of the content of the complement. In 7.4, the preceding context they’re French
clearly indicates that the content of the complement is false. The model
predicts $-0.28$ (the mean label for training items with wish under positive
polarity is $-0.5$), suggesting that the model fails to take the preceding
context into account.

. They’re French, but wish that they were${}^{\text{-2.5}\ [\text{-0.28}]}$
mostly Caribbean.

The effect of context can be less explicit, but nonetheless there. In 7.4, the
context which it’s mainly just when it gets real, real hot elaborates on the
time of the warnings, carrying the presupposition that the content of the
complement they have warnings here is true. In 7.4, the preceding context
Although Tarzan is now nominally in control, with the marker although and
nominally suggesting that Tarzan is not actually in charge, makes the
complement Kala the Ape-Mom is really in charge more likely.

. […] B: Oh, gosh, I think I would hate to live in California, the smog there.
A: Uh-huh. B: I mean, I can’t believe they have${}^{\text{2.33}\
[\text{0.327}]}$ warnings here, which it’s mainly just when it gets real, real
hot.

. Although Tarzan is now nominally in control, one does not suspect that Kala
the Ape-Mom, the Empress Dowager of the Jungle, is${}^{\text{2.5}\
[\text{-0.23}]}$ really in charge.

#### Discourse function

When sentences are uttered in a discourse, there is a discourse goal or a
question under discussion (QUD) that the sentence is trying to address Roberts
(2012). According to Tonhauser et al. (2018), the contents of embedded
complements that do not address the question under discussion are considered
as more factual than those that do address the QUD. Even for items that are
sentences in isolation, as in RP, readers interpreting these sentences
probably reconstruct a discourse and the implicit QUD that the sentences are
trying to address. For instance, 7.4 contains the factive verb see, but its
complement is labeled as nonfactual ($-2$).

. Jon did not see that they were${}^{\text{-2}\ [\text{1.45}]}$ hard pressed.

Such label is compatible with a QUD asking what is the evidence that Jon has
to whether they were hard pressed. The complement does not answer that QUD,
but the sentence affirms that Jon lacks visual evidence to conclude that they
were hard pressed. In 7.4, the embedded event is annotated as factual although
it is embedded under a report verb (tell). However, the sentence in 7.4 can be
understood as providing a partial answer to the QUD What was the vice
president told?. The content of the complement does not address the QUD, and
is therefore perceived as factual.

. The Vice President was not told that the Air Force was trying${}^{\text{2}\
[\text{-0.15}]}$ to protect the Secretary of State through a combat air patrol
over Washington.

#### Tense/aspect

The tense/aspect of the clause-embedding verb and/or the complement affects
the factuality of the content of the complement Karttunen (1971b); de Marneffe
et al. (2019). In 7.4, the past perfect had meant implies that the complement
did not happen ($-2.5$), whereas in 7.4 in the present tense, the complement
is interpreted as neutral ($0.5$).

. She had meant to warn${}^{\text{-2.5}\ [\text{-0.24}]}$ Mr. Brown about
Tuppence.

. A bigger contribution means to support${}^{\text{0.5}\ [\text{1.45}]}$
candidate Y.

#### Subject authority/credibility

The authority of the subject of the clause-embedding verb also affects
factuality judgments (i.a., Schlenker 2010, de Marneffe et al. 2012). The
subjects of , a legal document, and , the tenets of a religion, have the
authority to require or demand. Therefore what the legal document requires is
perceived as factual, and what the tenets do not demand is perceived as
nonfactual.

. Section 605(b) requires that the Chief Counsel gets${}^{\text{2.5}\
[\text{0.53}]}$ the statement.

. The tenets of Jainism do not demand that everyone must be wearing shoes when
they come into a holy place${}^{\text{-2}\ [\text{-0.28}]}$ .

On the other hand, the perceived lack of authority of the subject may suggest
that the embedded event is not factual. In 7.4, although remember is a factive
verb, the embedded event only receives a mean annotation of $1$, probably
because the subject a witness introduces a specific situational context
questioning whether to consider someone’s memories as facts.

. A witness remembered that there were${}^{\text{1}\ [\text{2.74}]}$ four
simultaneous decision making processes going on at once.

#### Subject-complement interaction for prospective events

Some clause-embedding verbs, such as decide and choose, introduce so-called
“prospective events” which could take place in the future (Saurí, 2008). The
likelihood that these events will actually take place depends on several
factors: the content of the complement itself, the embedding verb and the
subject of the verb. When the subject of the clause-embedding verb is the same
as the subject of the complement, the prospective events are often judged as
factual, as in 7.4. In 7.4, the subjects of the main verb and the complement
verb are different, and the complement is judged as neutral.

. He decided that he must leave no stone unturned${}^{\text{2.5}\
[\text{0.43}]}$ .

. Poirot decided that Miss Howard must be kept in the dark${}^{\text{0.5}\
[\text{1.49}]}$ .

Even when subjects are the same, the nature of the prospective event itself
also affects whether it is perceived as factual. Compare 7.4 and 7.4 both
featuring the construction do not choose to: 7.4 is judged as nonfactual
whereas 7.4 is neutral. This could be due to the difference in the extent to
which the subject entity has the ability to fulfill the chosen course of
action denoted by the embedded predicate. In 7.4, Hillary Clinton can be
perceived to be able to decide where to stay, and therefore when she does not
choose to stay somewhere, one infers that she indeed does not stay there. On
the other hand, the subject in 7.4 is not able to fulfill the chosen course of
action (where to be buried), since he is presumably dead.

. Hillary Clinton does not choose to stay${}^{\text{-2.5}\ [\text{-0.92}]}$ at
Trump Tower.

. He did not choose to be buried${}^{\text{0.5}\ [\text{-0.75}]}$ there.

#### Lexical inference

An error item is categorized under “lexical inference” if the gold label is
inline with the signature of its embedding verb. Such errors happen on items
of a given verb for which the training data do not exhibit a clear pattern
because the training items contains items where the verb follows its signature
as well as items where pragmatic factors override the signature
interpretation. For example, gets a factual interpretation, consistent with
the factive signature of see.

. He did not see that Manning had glanced${}^{\text{2}\ [\text{0.47}]}$ at
him.

However, the training instances with see under negation have labels ranging
from $-2$ to $2$ (see the orange ×’s in the fourth panel of Figure 3). Some
items indeed get a negative label because of the presence of pragmatic
factors, such as in 7.4, but the system is unable to identify these factors.
It thus fails to learn to tease apart the factual and nonfactual items,
predicting a neutral label that is roughly the mean of the labels of the
training items with see under negation.

#### Annotation error

As in all datasets, it seems that some human labels are wrong and the model
actually predicts the right label. For instance, 7.4 should have a more
positive label (rather than $0.5$), as realize is taken to be factive and
nothing in the context indicates a nonfactual interpretation.

. I did not realize that John had fought${}^{\text{0.5}\ [\text{2.31}]}$ with
his mother prior to killing her.

In total, 55 items (with absolute errors ranging from 1.10 to 4.35, and a mean
of 1.95) were annotated in CB out of 556 items, and 250 in RP (with absolute
errors ranging from 1.23 to 4.36, and a mean of 1.70) out of 2,508 items.
Table 7 gives the numbers and percentages of errors in each category. The two
datasets show different patterns that reflect their own characteristics. CB
has rich preceding contexts, and therefore more items exhibit inferences that
can be traced to the effect of context. RP has more item categorized under
lexical inference, because there is not much context to override the default
lexical inference. RP also has more items under annotation errors, due to the
limited amount of annotations collected for each item (3 annotations per
item).

| CB |  | RP
---|---|---|---
| # | % |  | # | %
Prior probability of the event | 5 | 9.1 |  | 32 | 12.8
Context suggests (non)factuality | 34 | 61.8 |  | 29 | 11.6
Question Under Discussion (QUD) |  |  |  | 20 | 8.0
Tense/aspect | 1 | 1.8 |  | 8 | 3.2
Subject authority/credibility | 1 | 1.8 |  | 14 | 5.6
Subject-complement interaction |  |  |  | 26 | 10.4
Lexical inference | 12 | 21.8 |  | 88 | 35.2
Annotation error | 2 | 3.6 |  | 33 | 13.2
Total items categorized | 55 |  |  | 250 |

Table 7:  Numbers (#) and percentages (%) of error items categorized for CB
and RP.

Although we only systematically annotated CB and RP (given that these datasets
focus on embedded events), the errors in the other datasets focusing on main-
clause events also exhibit similar inferences as the ones we categorized
above, such as effects of context and lexical inference (more broadly
construed).161616Some of the error categories only apply to embedded events,
including the effect of Question Under Discussion and subject authority. Most
of the errors concern nominal events. In the following examples, 7.4 and 7.4
from UW, and 7.4 from MEANTIME, the model failed to take into account the
surrounding context which suggests that the events are nonfactual. In 7.4, the
lexical meaning of dropped clearly indicates that the plan is nonfactual. In
7.4, the death was faked, and in 7.4 production was brought to an end,
indicating that the death did not happen and there is no production anymore.

. In 2011 , the AAR consortium attempted to block a drilling joint venture in
the Arctic between BP and Rosneft through the courts and the
plan${}^{\text{-2.8}\ [\text{1.84}]}$ was eventually dropped.

. The day before Raymond Roth was pulled over, his wife, Evana, showed
authorities e-mails she had discovered that appeared to detail a plan between
him and his son to fake his death${}^{\text{-2.8}\ [\text{1.35}]}$

. Boeing Commercial Airplanes on Tuesday delivered the final 717 jet built to
AirTran Airways in ceremonies in Long Beach, California, bringing
production${}^{\text{-3}\ [\text{3.02}]}$ of McDonnell Douglas jets to an end.

In 7.4, from FactBank, just what NATO will do carries the implication that
NATO will do something, and the do event is therefore annotated as factual.

. Just what NATO will do${}^{\text{3}\ [\text{-0.05}]}$ with these eager
applicants is not clear.

Example 7.4 from UDS-IH2 features a specific meaning of the embedding verb
say: here say makes an assumption instead of the usual speech report, and
therefore suggests that the embedded event is not factual.

. Say after I finished${}^{\text{-2.25}\ [\text{2.38}]}$ those 2 years and I
found a job.

#### Inter-annotator agreement for categorization

Both annotators annotated all 55 items in CB. For RP, one of the annotators
annotated 190 examples, and the other annotated 100 examples, with 40
annotated by both. Among the set of items that were annotated by both
annotators, annotators agreed on the error categorization 90% of the time for
the CB items and 80% of the time for the RP items. This is comparable to the
agreement level in Williams et al. (2020), in which inferences types for the
ANLI dataset Nie et al. (2020) are annotated.

##  8 Conclusion

In this paper, we showed that, although fine-tuning BERT gives strong
performance on several factuality datasets, it only captures statistical
regularities in the data and fails to take into account pragmatic factors
which play a role on event factuality. This aligns with Chaves (2020)’s
findings for acceptability of filler-gap dependencies: neural models give the
impression that they capture island constraints well when such phenomena can
be predicted by surface statistical regularities, but the models do not
actually capture the underlying mechanism involving various semantic and
pragmatic factors. Recent work has found that BERT models have some capacity
to perform pragmatic inferences: Schuster et al. (2020) for scalar
implicatures in naturally occurring data, Jeretič et al. (2020) for scalar
implicatures and presuppositions triggered by certain lexical items in
constructed data. It is however possible that the good performance on those
data is solely driven by surface features as well. BERT models still only have
limited capabilities to account for the wide range of pragmatic inferences in
human language.

## Acknowledgment

We thank TACL editor-in-chief Brian Roark and action editor Benjamin Van Durme
for the time they committed to the review process, as well as the anonymous
reviewers for their insightful feedback. We also thank Micha Elsner, Cory
Shain, Michael White, and members of the OSU Clippers discussion group for
suggestions and feedback. This material is based upon work supported by the
National Science Foundation under Grant No. IIS-1845122.

## References

  * Beaver (2010) David Beaver. 2010.  Have you noticed that your belly button lint colour is related to the colour of your clothing?  In Rainer Bäuerle, Uwe Reyle, and Thomas Ede Zimmermann, editors, _Presuppositions and Discourse: Essays Offered to Hans Kamp_ , pages 65–99. Leiden, The Netherlands: Brill.
  * Bies et al. (2012) Ann Bies, Justin Mott, Colin Warner, and Seth Kulick. 2012.  English Web Treebank.  _Linguistic Data Consortium, Philadelphia, PA_.
  * Chaves (2020) Rui P. Chaves. 2020.  What don’t RNN language models learn about filler-gap dependencies?  _Proceedings of the Society for Computation in Linguistics_ , 3(1):20–30.
  * Chierchia and McConnell-Ginet (1990) Gennaro Chierchia and Sally McConnell-Ginet. 1990.  Meaning and grammar.
  * Gardner et al. (2018) Matt Gardner, Joel Grus, Mark Neumann, Oyvind Tafjord, Pradeep Dasigi, Nelson F. Liu, Matthew Peters, Michael Schmitz, and Luke Zettlemoyer. 2018.  AllenNLP: A deep semantic natural language processing platform.  In _Proceedings of Workshop for NLP Open Source Software (NLP-OSS)_ , pages 1–6, Melbourne, Australia. Association for Computational Linguistics.
  * Jeretič et al. (2020) Paloma Jeretič, Alex Warstadt, Suvrat Bhooshan, and Adina Williams. 2020.  Are natural language inference models IMPPRESsive? Learning IMPlicature and PRESupposition.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_. Association for Computational Linguistics.
  * Jiang and de Marneffe (2019a) Nanjiang Jiang and Marie-Catherine de Marneffe. 2019a.  Do you know that Florence is packed with visitors? evaluating state-of-the-art models of speaker commitment.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 4208–4213, Florence, Italy. Association for Computational Linguistics.
  * Jiang and de Marneffe (2019b) Nanjiang Jiang and Marie-Catherine de Marneffe. 2019b.  Evaluating BERT for natural language inference: A case study on the CommitmentBank.  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 6086–6091, Hong Kong, China. Association for Computational Linguistics.
  * Karttunen (1971a) Lauri Karttunen. 1971a.  Implicative verbs.  _Language_ , 47(2):340–358.
  * Karttunen (1971b) Lauri Karttunen. 1971b.  Some observations on factivity.  _Paper in Linguistics_ , 4(1):55–69.
  * Karttunen (2012) Lauri Karttunen. 2012.  Simple and phrasal implicatives.  In _Proceedings of the First Joint Conference on Lexical and Computational Semantics - Volume 1: Proceedings of the Main Conference and the Shared Task, and Volume 2: Proceedings of the Sixth International Workshop on Semantic Evaluation_ , pages 124–131.
  * Kiparsky and Kiparsky (1970) Paul Kiparsky and Carol Kiparsky. 1970.  Fact.  In M. Bierwisch and K.E. Heidolph, editors, _Progress in linguistics_ , pages 143–173. Mouton, The Hague, Paris.
  * Lee et al. (2015) Kenton Lee, Yoav Artzi, Yejin Choi, and Luke Zettlemoyer. 2015.  Event detection and factuality assessment with non-expert supervision.  In _Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing_ , pages 1643–1648.
  * 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.  _CoRR_ , abs/1907.11692.
  * de Marneffe et al. (2012) Marie-Catherine de Marneffe, Christopher D. Manning, and Christopher Potts. 2012\.  Did it happen? the pragmatic complexity of veridicality assessment.  _Computational Linguistics_ , 38(2):301–333.
  * de Marneffe et al. (2019) Marie-Catherine de Marneffe, Mandy Simons, and Judith Tonhauser. 2019.  The CommitmentBank: Investigating projection in naturally occurring discourse.  In _Sinn und Bedeutung 23_.
  * Minard et al. (2016) Anne-Lyse Myriam Minard, Manuela Speranza, Ruben Urizar, Begona Altuna, Marieke van Erp, Anneleen Schoen, and Chantal van Son. 2016.  MEANTIME, the newsreader multilingual event and time corpus.  In _Proceedings of the 10th International Conference on Language Resources and Evaluation (LREC 2016)_ , pages 4417–4422.
  * Nairn et al. (2006) Rowan Nairn, Cleo Condoravdi, and Lauri Karttunen. 2006.  Computing relative polarity for textual inference.  In _Proceedings of the Fifth International Workshop on Inference in Computational Semantics (ICoS-5)_.
  * Nie et al. (2020) Yixin Nie, Adina Williams, Emily Dinan, Mohit Bansal, Jason Weston, and Douwe Kiela. 2020.  Adversarial NLI: A new benchmark for natural language understanding.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_. Association for Computational Linguistics.
  * Poliak et al. (2018) Adam Poliak, Aparajita Haldar, Rachel Rudinger, J. Edward Hu, Ellie Pavlick, Aaron Steven White, and Benjamin Van Durme. 2018.  Collecting diverse natural language inference problems for sentence representation evaluation.  In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , pages 67–81, Brussels, Belgium. Association for Computational Linguistics.
  * Pouran Ben Veyseh et al. (2019) Amir Pouran Ben Veyseh, Thien Huu Nguyen, and Dejing Dou. 2019.  Graph based neural networks for event factuality prediction using syntactic and semantic structures.  In _Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics_ , pages 4393–4399, Florence, Italy. Association for Computational Linguistics.
  * Roberts (2012) Craige Roberts. 2012.  Information structure in discourse: Towards an integrated formal theory of pragmatics.  _Semantics and Pragmatics_ , 5(6):1–69.
  * Ross and Pavlick (2019) Alexis Ross and Ellie Pavlick. 2019.  How well do NLI models capture verb veridicality? 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 2230–2240, Hong Kong, China. Association for Computational Linguistics.
  * Rudinger et al. (2018) Rachel Rudinger, Aaron Steven White, and Benjamin Van Durme. 2018.  Neural models of factuality.  In _Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics_ , pages 731–744.
  * Saurí (2008) Roser Saurí. 2008.  FactBank 1.0. annotation guidelines.
  * Saurí and Pustejovsky (2009) Roser Saurí and James Pustejovsky. 2009.  FactBank: A corpus annotated with event factuality.  _Language resources and evaluation_ , 43(3):227.
  * Schlenker (2010) Philippe Schlenker. 2010.  Local contexts and local meanings.  _Philosophical Studies_ , 151(1):115–142.
  * Schuster et al. (2020) Sebastian Schuster, Yuxing Chen, and Judith Degen. 2020.  Harnessing the richness of the linguistic signal in predicting pragmatic inferences.  In _Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics_. Association for Computational Linguistics.
  * Simons et al. (2010) Mandy Simons, Judith Tonhauser, David Beaver, and Craige Roberts. 2010.  What projects and why.  In _Proceedings of Semantics and Linguistic Theory 20_. CLC Publications.
  * Smith and Hall (2014) E Allyn Smith and Kathleen Currie Hall. 2014.  The relationship between projection and embedding environment.  In _Proceedings of the 48th Meeting of the Chicago Linguistics Society_. Citeseer.
  * Stanovsky et al. (2017) Gabriel Stanovsky, Judith Eckle-Kohler, Yevgeniy Puzikov, Ido Dagan, and Iryna Gurevych. 2017.  Integrating deep linguistic features in factuality prediction over unified datasets.  In _Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)_ , pages 352–357.
  * Tonhauser et al. (2018) Judith Tonhauser, David I Beaver, and Judith Degen. 2018.  How projective is projective content? Gradience in projectivity and at-issueness.  _Journal of Semantics_ , 35(3):495–542.
  * Wang et al. (2019) Alex Wang, Ian F. Tenney, Yada Pruksachatkun, Phil Yeres, Jason Phang, Haokun Liu, Phu Mon Htut, , Katherin Yu, Jan Hula, Patrick Xia, Raghu Pappagari, Shuning Jin, R. Thomas McCoy, Roma Patel, Yinghui Huang, Edouard Grave, Najoung Kim, Thibault Févry, Berlin Chen, Nikita Nangia, Anhad Mohananey, Katharina Kann, Shikha Bordia, Nicolas Patry, David Benton, Ellie Pavlick, and Samuel R. Bowman. 2019.  jiant 1.3: A software toolkit for research on general-purpose text understanding models.  http://jiant.info/.
  * White et al. (2016) Aaron Steven White, Drew Reisinger, Keisuke Sakaguchi, Tim Vieira, Sheng Zhang, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme. 2016.  Universal decompositional semantics on Universal Dependencies.  In _Proceedings of the 2016 Conference on Empirical Methods in Natural Language Processing_ , pages 1713–1723, Austin, Texas. Association for Computational Linguistics.
  * White et al. (2018) Aaron Steven White, Rachel Rudinger, Kyle Rawlins, and Benjamin Van Durme. 2018\.  Lexicosyntactic inference in neural models.  In _Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing_ , pages 4717–4724, Brussels, Belgium. Association for Computational Linguistics.
  * Williams et al. (2018) Adina Williams, Nikita Nangia, and Samuel Bowman. 2018.  A broad-coverage challenge corpus for sentence understanding through inference.  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 1112–1122. Association for Computational Linguistics.
  * Williams et al. (2020) Adina Williams, Tristan Thrush, and Douwe Kiela. 2020.  Anlizing the adversarial natural language inference dataset.  _CoRR_ , abs/2010.12729.