# Towards Weakly-Supervised Hate Speech
Classification Across Datasets

Yiping Jin1,2, Leo Wanner3,1, Vishakha Laxman Kadam2, Alexander Shvets1
1NLP Group, Pompeu Fabra University, Barcelona, Spain
2Knorex, 02-129 WeWork Futura, Pune, India
3Catalan Institute for Research and Advanced Studies
{yiping.jin, leo.wanner<EMAIL_ADDRESS>
<EMAIL_ADDRESS>

###### Abstract

As pointed out by several scholars, current research on hate speech (HS)
recognition is characterized by unsystematic data creation strategies and
diverging annotation schemata. Subsequently, supervised-learning models tend
to generalize poorly to datasets they were not trained on, and the performance
of the models trained on datasets labeled using different HS taxonomies cannot
be compared. To ease this problem, we propose applying extremely weak
supervision that only relies on the class name rather than on class samples
from the annotated data. We demonstrate the effectiveness of a state-of-the-
art weakly-supervised text classification model in various in-dataset and
cross-dataset settings. Furthermore, we conduct an in-depth quantitative and
qualitative analysis of the source of poor generalizability of HS
classification models.

Content Warning: This document discusses examples of harmful content (hate,
abuse, and negative stereotypes). The authors do not support the use of
harmful language.

##  1 Introduction

Due to a growing concern about its impact on society, hate speech (HS)
recognition recently received much attention from the NLP research community
(Bilewicz and Soral, 2020). A large number of proposals on how to address HS
as a supervised classification task have been put forward; see, among others,
(Waseem and Hovy, 2016; Waseem, 2016; Poletto et al., 2021) and several shared
tasks have been organized (Basile et al., 2019; Caselli et al., 2020).

However, while Transformers models such as BERT (Devlin et al., 2019) achieved
impressive performance on various benchmark datasets (Swamy et al., 2019),
recent work demonstrated that state-of-the-art HS classification models
generalize poorly to datasets other than the ones they are trained on (Fortuna
et al., 2020, 2021; Yin and Zubiaga, 2021), even when the datasets come from
the same data source, e.g., Twitter. This casts a doubt on what we have
achieved in the HS classification task.

Fortuna et al. (2022) identify three main challenges related to HS
classification: 1. the definitorial challenge: while the interpretation of
what is HS highly depends on the cultural and social norms of its creator
(Talat et al., 2022), HS research favours a universal definition; 2. the
annotation challenge: due to the subjective nature of HS, the annotation also
often depends on the context, the social bias of the annotator, and their
familiarity with the topic (Wiegand et al., 2019), such that the annotators
with different backgrounds tend to provide deviating annotations (Waseem,
2016; Olteanu et al., 2018), especially when not only the presence of HS is to
be annotated, but also its category and the group it targets (Basile et al.,
2019); 3. the learning and evaluation challenge: the common evaluation
practice of the HS classification models assumes that the distributions of the
training data and the data to which the model is applied are identical, which
is not the case in reality; real-world HS data is relatively rare, while the
strategies applied for the creation of HS datasets favor explicit HS
expressions (Sap et al., 2020; Yin and Zubiaga, 2021), using search with
explicit target keywords Waseem and Hovy (2016); Basile et al. (2019).

In order to address these challenges, we propose the use of extremely weak
supervision, which uses category names as the only supervision signal (Meng et
al., 2020; Wang et al., 2021): Extremely weak supervision does not presuppose
any definition of HS, which would guide the annotation, such that when the
interpretation of what is to be considered as HS is modified, we can retrain
the model on the same dataset, without the need of re-annotation. Furthermore,
when the data distribution changes, the model can learn from unlabeled data
and adapt to a new domain.

Our contributions can be summarized as follows:

  * •

We apply extremely weak supervision to HS classification and achieve promising
performance compared to fully-supervised and weakly-supervised baselines.

  * •

We perform cross-dataset classification under different settings and yield
insights on the transferability of HS datasets and models.

  * •

We conduct an in-depth analysis and highlight the potentials and limitations
of weak supervision for HS classification.

##  2 Related Work

Since our goal is to advance the research on HS classification, we focus, in
what follows, on the review of related work in this area and refrain from the
discussion of the application of weakly supervised supervision models to other
problems.

Standardizing different hate speech (HS) taxonomies across datasets is a first
step in performing cross-dataset analysis and experiments. To this end,
Fortuna et al. (2020) created a category mapping among six publicly available
HS datasets. Furthermore, they measured the data similarity of categories in
an intra- and inter-dataset manner and reported the performance of a public HS
classification API on different datasets and categories.

Other previous work in cross-dataset HS classification followed similar
experimental settings by training a supervised classifier on the training set
of each dataset and reporting the performance on the corresponding test set
and test sets from other datasets. For instance, Karan and Šnajder (2018)
trained linear SVM models on 9 different HS datasets. They showed that models
performed considerably worse on out-of-domain datasets. They further performed
domain adaptation using the FEDA framework (Daumé III, 2007) and demonstrated
that having at least some in-domain data is crucial for achieving good
performance. Similarly, Swamy et al. (2019) compared Linear SVM, LSTM, and
BERT models trained on different datasets. They reported that some pairs of
datasets perform well on each other, likely due to a high degree of overlap.
They also claimed that a more balanced class ratio is essential for the
datasets’ generalizability.

Fortuna et al. (2021) conducted a large-scale cross-dataset experiment by
training a total of 1,698 classifiers using different algorithms, datasets,
and other experimental setups. They demonstrated that the generalizability
does not only depend on the dataset but also on the model. Transformer-based
models have a better potential to generalize to other datasets, likely thanks
to the wealth of data they have observed during pre-training. Furthermore,
they built a random forest classifier to predict the generalizability based on
human-engineered dataset features. The experiment revealed that to achieve
cross-dataset generalization, the model must first perform well in an intra-
dataset scenario. In addition, inconsistency in class definition hampers
generalizability.

Wiegand et al. (2019) and Arango et al. (2019) studied the impact of data bias
on the generalizability of HS models, with the outcome that popular benchmark
datasets possess several sources of biases, such as bias towards explicit HS
expressions, topic bias, and author bias. The classification results dropped
significantly when the bias is reduced. To this end, they proposed using
cross-dataset classification as a way to evaluate models’ performance in a
more realistic setting.

Gao et al. (2017) argued that the low frequency of online HS impedes obtaining
a wide-coverage HS detection dataset. To this end, they proposed a two-path
bootstrapping approach involving an explicit slur term learner and an LSTM
(Hochreiter and Schmidhuber, 1997) classifier. The slur term learner is
initialized with a list of hand-engineered seed slur terms and applies to an
unlabeled dataset to automatically label hateful posts, which are used to
train the classifier. The slur term learner and the classifier are trained
iteratively in a co-training manner (Blum and Mitchell, 1998).

A distinct approach was proposed by Talat et al. (2018). This approach
utilized multi-task learning (MTL) to enhance domain robustness. They trained
a classifier on three distinct sets of annotations: Waseem and Hovy (2016),
Waseem (2016), and Davidson et al. (2017). While MTL helps to prevent
overfitting and may provide auxiliary fine-grained predictions, it requires
annotating a dataset using different taxonomies, granularities, or aspects.

Our approach is most similar to Jin et al. (2022)’s, which also applied
weakly-supervised learning on a target-domain dataset. However, their approach
requires mining a list of 30 high-quality keywords for each category from a
large labeled source-domain dataset. Moreover, they assume that the source and
target datasets are labeled using the same HS taxonomy.

##  3 Weakly-Supervised HS Classification

In this section, we briefly introduce the basics of weakly supervised text
classification and then discuss the cross-dataset classification we aim for.

###  3.1 Preliminaries: Weakly Supervised Text Classification

Weakly-supervised text classification eliminates the need for a large labeled
dataset (Meng et al., 2018; Mekala and Shang, 2020). Instead, it trains
classifiers using a handful of labeled seed words and unlabeled documents.
While the human annotation effort is significantly reduced, weakly-supervised
classification methods are sensitive to the choice of seed words, and the
process to nominate high-quality seed words is non-trivial (Jin et al., 2021).

More recently, Meng et al. (2020) and Wang et al. (2021) explored extremely
weak supervision, where the model is given only the category name instead of
manually curated seed words. Extremely weak supervision is well suited for
hate speech detection because we may not know all the aspects of hate speech
for a particular category or target group, or what a user may interpret as a
HS statement that falls into a specific category. On top of that, extremely
weak supervision often performs semantic expansion on the unlabeled dataset
and automatically augments the category representation with new aspects (in
the form of seed words).

We choose X-Class (Wang et al., 2021) as the primary weakly-supervised
classification method, which matches or outperforms previous state-of-the-art
weakly-supervised methods on 7 benchmark datasets. X-Class first estimates
category representations by incorporating words similar to each category. It
represents each word by its averaged contextualized word embedding across the
entire dataset. It then iteratively adds the most similar word to each
category whose embedding has the highest cosine similarity to the category
representation. The category representation is updated as a weighted average
of the expanded keywords. Expressly, they assume a Zipf’s law distribution
(Powers, 1998) and weight the $j$-th keyword by 1/$j$.

$s_{\ell}=\frac{\sum^{|\kappa_{\ell}|}_{j=1}1/j\cdot
s_{\kappa_{\ell,j}}}{\sum^{|\kappa_{\ell}|}_{j=1}1/j}$ (1)

where $\boldsymbol{\kappa_{\ell,j}}$ is the $j$-th keyword of category
$\boldsymbol{\ell}$ and $\boldsymbol{s_{\kappa_{\ell,j}}}$ is its average
contextualized embedding. X-Class also performs a consistency check and stops
adding new words if a category’s nearest words have changed.

Then, X-Class derives document $\boldsymbol{i}$’s category-oriented document
representation $\boldsymbol{d_{i}}$ by weighting each word in the document
based on its similarity to the category representations. Afterward, it
clusters the documents using a Gaussian Mixture Model (GMM) (Duda and Hart,
1973) by initializing the category representations as cluster centroids.
Finally, the most confident pseudo-labeled documents from each cluster are
used to train a text classifier.

In our initial experiments, we observed that while GMM generally improves the
pseudo-labeling, the accuracy for some low-frequency categories tends to drop
sharply. This is likely because GMM works as a global density estimator.
Therefore, data of the more frequent categories may “attract” more weights and
cause the category representation for low-frequency categories to diverge too
much from its initial category representation. To address this problem, we
introduce an additional representation-based prediction, which assigns
document $\boldsymbol{i}$ to the category representation which has the highest
cosine similarity:

$\ell_{i}^{rep}=\operatorname*{arg\,max}_{\ell\in L}cosine(s_{\ell},d_{i})$
(2)

We denote GMM’s category assignment for document $\boldsymbol{i}$ as
‘$\ell_{i}^{gmm}$’. Instead of pseudo-labeling most confident documents based
on GMM only, we take the subset of confident documents to which GMM and
representation-based prediction assign the same label
($\ell_{i}^{gmm}=\ell_{i}^{rep}$). This ensures that the document is
sufficiently close to the original category representation. We denote this
modified version as ‘X-ClassAgree’.

###  3.2 Cross-Dataset Classification

In this work, we study cross-dataset classification, where we do not have any
document labels in the target dataset. A dataset is characterized by its
documents (and their underlying topics and word distributions) and taxonomy
(list of categories).111While the term “cross-domain” is more popular than
“cross-dataset”, it does not suggest that the source and target dataset’s
taxonomies may differ. The discussion of the related problem of cross-task
generalization (Raffel et al., 2020; Sanh et al., 2022), which works for
unrelated tasks, is beyond the scope of this work.

Given a single HS dataset with its corresponding categories, we can
straightforwardly apply X-Class using the category names and an unlabeled
dataset. On the other hand, both the data distribution and taxonomy may differ
when we experiment on different datasets. There are three different cases for
the relation between the taxonomies of the source and target datasets.

  * •

1-to-1: The target taxonomy is identical to the source taxonomy or a subset of
it.

  * •

N-to-1: The target taxonomy differs from the source taxonomy, but each target
category can be mapped to one or more source categories.

  * •

N-to-N: The target taxonomy differs from the source taxonomy, and some target
categories cannot be mapped to any source category.

Supervised learning can be applied in the first two cases: We can create a
category mapping from the target categories to the source categories, then use
this mapping to either post-process the model predictions (converting
predicted source categories to target categories) or relabel the dataset using
the target taxonomy and retrain the model. However, in the last case, we
cannot directly apply supervised learning without further data collection and
annotation because we lack labeled data for at least some categories. In
contrast, weakly-supervised methods do not require labeled documents and can
readily utilize unlabeled documents in the target dataset to capture the
underlying distribution. Furthermore, even when applied to a completely unseen
dataset, it can also “relabel” the source dataset using the target taxonomy
and bootstrap a classifier.

##  4 Experiments

###  4.1 Datasets

We conduct experiments on two popular HS datasets that differ with respect to
the data source and taxonomy of HS categories: the Waseem dataset and the SBIC
dataset. The Waseem dataset (Waseem and Hovy,
2016)222https://github.com/zeeraktalat/hatespeech contains 5,355 tweets with
sexist and racist content. The dataset was annotated by the authors (inter-
annotator agreement $\kappa=0.84$) and reviewed by a domain expert (a gender
studies student who is a non-activist feminist). The SBIC dataset (Sap et al.,
2020)333https://maartensap.com/social-bias-frames/ contains 44,671 posts
collected from different domains: Reddit, Twitter, and hate sites. It was
annotated by crowdsource workers on Amazon Mechanical Turk. A small portion of
the data is originally from the Waseem dataset (1,816 posts). We exclude these
posts to avoid overlap between the two datasets.

SBIC dataset does not set a predefined taxonomy for HS categories. Instead,
annotators can indicate the target group with free-text answers. We select the
most frequent six target groups that can be mapped to the categories in the
Waseem dataset. While our proposed weakly-supervised learning method does not
depend on category mapping, we select the SBIC categories that can be mapped
to compare with supervised learning baselines. Table 1 shows this category
mapping.

Waseem |  SBIC
---|---
Sexist |  Women; LGBT
Racist |  Black; Jewish; Muslim; Asian
Table 1: Category mapping between the Waseem and SBIC datasets.

We use the original train/dev/test split (75%/12.5%/12.5%) in the SBIC dataset
and randomly split the Waseem dataset to 90%/10% into training and test sets.
We apply standard preprocessing following Barbieri et al. (2020), including
user mention anonymization and website links and emoji removal. Table 2
presents the distribution of the posts in the two datasets.

Dataset |  Category | # Train | # Test
---|---|---|---
Waseem |  Sexist | 3,107 | 323
Racist | 1,799 | 177
|  Subtotal | 4,906 | 500
SBIC |  Women | 2,594 | 351
Black folks | 2,512 | 576
Jewish folks | 847 | 207
LGBT folks | 490 | 53
Muslim folks | 412 | 85
Asian folks | 224 | 34
|  Subtotal | 7,079 | 1,306
Table 2: Distribution of the documents per dataset, with the posts that
contain no word after post-processing removed. The average number of words per
post in the Waseem dataset is 17.1 and in the SBIC dataset 20.0.

###  4.2 Compared Methods

We compare X-Class with two representative supervised learning baselines which
are trained using the full labeled training dataset:

  * •

Support Vector Machines (SVM) (Cortes and Vapnik, 1995): We use scikit-
learn’s444https://scikit-learn.org linear SGD classifier with default hyper-
parameters and tf-idf weighting.

  * •

BERT (Devlin et al., 2019): We fine-tune the bert-base-uncased
checkpoint555https://huggingface.co/bert-base-uncased using the exact hyper-
parameters to train the final classifier in X-Class (detailed in Section 4.3).

We also compare the performance of our model with the following baselines that
do not require any document labeling:666We provide the weakly-supervised
learning baselines the full unlabeled training dataset for keyword expansion
and pseudo-labeling.

  * •

Majority class: Always predict the most frequent category in the training
dataset.

  * •

Keyword voting (category name): Assign the category whose category name occurs
most frequently in the document. Fall back to the majority class prediction if
there is a tie or none of the keywords appear.

  * •

Keyword voting (X-Class keywords): Same as above, but use the expanded
keywords in X-Class’s category representation and their associated weights.
Assign the category which receives the highest score.

  * •

Zero-shot PET (Schick and Schütze, 2021a): Prompting a pre-trained BERT model
using hand-crafted patterns and verbalizers to classify documents. We provide
details of this baseline in Appendix B.

  * •

WESTCLASS (Meng et al., 2018)777https://github.com/yumeng5/WeSTClass: CNN-
based neural text classifier. It first generates pseudo documents with a
generative model seeded with user-provided keywords for pre-training, then
conducts self-training to bootstrap from unlabeled documents. We use three
manually curated seed words for each category following Meng et al. (2018).

  * •

LOTClass (Meng et al., 2020)888https://github.com/yumeng5/LOTClass: A strong
baseline using extremely weak supervision. The model first uses a masked
language model to expand keywords from the category names, then mines
category-indicative words using a novel masked category prediction task.
Finally, it generalizes via self-training.

###  4.3 Experiment Settings

We use the official implementation of
X-Class.999https://github.com/ZihanWangKi/XClass The bert-base-uncased
checkpoint is used to calculate the document representation and fine-tune the
final classifier; the maximum number of keywords for each category is set to
100; and the 50% most confident pseudo-labeled documents from each category
are used to train the final classifier.

To facilitate a fair comparison with supervised learning methods, we
reimplemented the final classifier fine-tuning step using the HuggingFace
Transformers
trainer101010https://huggingface.co/docs/transformers/main/training and
performed a minimum manual hyper-parameter tuning (learning_rate=2e-5;
num_epochs=6; weight_decay=0.05) on the SBIC dev set and applied them on both
datasets. We set the max_length and batch_size to 64.

We merged the following original target groups in the SBIC corpus into “LGBT
folks”: “gay men”, “lesbian women, gay men”, “lesbian women”, “trans women,
trans men”, “trans women”. Table 3 presents the category names used by the
models. We use the original category name except for “LGBT” because it does
not occur in the dataset. Instead, we use “gay”, the most frequently targeted
subgroup in the dataset. As shown in Appendix A, X-Class expands to keywords
representing other subgroups in the LGBT community.

Class |  Seed | Count |  WESTCLASS
---|---|---|---
Sexist |  sexist | 1,071 |  sexist sexism
misogynist
Racist |  racist | 33 |  racist racists racism
Women |  women | 652 |  women woman female
Black |  black | 1,601 |  black blacks
n*gro
Jewish |  jewish | 142 |  jewish jews jew
LGBT |  gay | 209 |  gay gays
homosexual
Muslim |  muslim | 228 |  muslim muslims
islamic
Asian |  asian | 121 |  asian asians
chinese
Table 3: Seed words used for each category and their frequency in the training
dataset. We manually curated the seed words in X-Class’s category
representation and select the top-3 ranked keywords to train WESTCLASS.

###  4.4 Results of the Experiments

We report the accuracy and macro P/R/F1 scores to quantify each method’s
performance.

In-Dataset Classification. We first validate the efficacy of the methods using
the standard in-dataset setting, providing the corresponding training and test
datasets. Table 4 displays the result.

Waseem Dataset
---
Model | Acc | P/R/F1
SVM | 97.2 | 97.1/96.8/96.9
BERT | 98.2 | 98.2/97.8/98.0
Majority class | 64.6 | 33.2/50.0/39.2
KV (class name) | 64.6 | 57.3/50.1/39.8
KV (X-Class) | 67.0 | 76.9/53.6/47.0
Zero-shot PET | 49.2 | 66.7/59.9/47.3
WESTCLASS | 77.8 | 77.8/80.4/77.3
LOTClass | 63.2 | 71.3/70.2/63.2
X-Class | 96.2 | 96.9/94.9/95.8
X-ClassAgree | 96.6 | 97.5/95.2/96.2
SBIC Dataset
Model | Acc | P/R/F1
SVM | 90.7 | 93.2/82.5/86.7
BERT | 95.7 | 94.2/95.1/94.6
Majority class | 26.9 | 4.5/16.7/7.1
KV (class name) | 57.7 |  85.2/39.7/41.9
KV (X-Class) | 55.2 | 47.8/45.1/40.8
Zero-shot PET | 35.1 | 38.4/21.6/15.8
WESTCLASS | 36.4 | 35.9/34.5/29.9
LOTClass | 54.2 | 29.2/29.3/27.5
X-Class | 79.8 | 74.0/81.8/74.8
X-ClassAgree | 81.4 | 76.1/85.3/76.6
Table 4: In-Dataset performance of various models. We highlight the best
performances of supervised and weakly-supervised methods in bold.

As expected, BERT outperformed SVM among the supervised-learning baselines on
both datasets. Interestingly, keyword voting using only the category name
achieved high precision for the SBIC dataset. However, its recall is much
lower than that of X-Class due to variations of expressions within the same
category. Using X-Class keywords improved keyword voting’s recall by 3.5% and
5.4% on the two datasets. However, the precision dropped significantly on the
SBIC dataset, likely due to the noisier keywords.

WESTCLASS performs superior to keyword voting baselines on the Waseem dataset,
primarily due to its high recall of the “Racist” category. This demonstrates
the advantage of semantic representation in neural models. However, its
performance pales on the SBIC dataset, revealing its weakness in handling more
complex cases that involve class imbalance and overlapping, which has been
discussed in Wang et al. (2021) and Jin et al. (2022). LOTClass demonstrates a
similar trend, but performs worse on both datasets.111111LOTClass has a higher
accuracy on SBIC dataset because it predicts the vast majority of the
documents to the most frequent categories “Women” and “Black”.  We analyze the
pseudo-labeling accuracy of weakly-supervised baselines and X-Class in
Appendix C.

Comparing X-Class and X-ClassAgree, we can see that our modification
consistently improved the performance.

Cross-Dataset Classification. We conduct cross-dataset classification using
the strongest supervised and weakly-supervised models and show the result in
Table 5. Note that for the “Waseem $\rightarrow$ SBIC” setting, we cannot
create a category mapping since the target dataset has more fine-grained
categories. Therefore, supervised methods and X-Class using category mapping
to post-process the predictions are not applicable.

When we train BERT and X-Class using only source-dataset documents, they both
perform worse on the target dataset than the in-dataset results in Table 4.
The performance drop is smaller for “SBIC $\rightarrow$ Waseem”, likely
because SBIC dataset contains representative posts for the Waseem categories.

SBIC $\rightarrow$ Waseem
---
Model | Acc | P/R/F1
BERT (post-process) | 93.6 |  92.4/94.7/93.2
BERT (retrain) | 93.6 |  92.4/94.2/93.2
X-Class (post-process) | 91.6 | 93.5/88.5/90.3
X-Class (retrain) | 84.4 | 89.9/78.1/80.6
X-ClassAgree (post-process) | 92.8 |  94.5/90.1/91.8
X-ClassAgree (retrain) | 92.6 | 93.4/90.4/91.6
Waseem $\rightarrow$ SBIC
Model | Acc | P/R/F1
X-Class (retrain) | 60.7 | 61.3/59.8/54.5
X-ClassAgree (retrain) | 69.8 |  62.7/62.2/58.3
Table 5: Cross-dataset performance of BERT and X-Class. Both models are
trained using source dataset documents and tested on the target dataset. We
highlight the best performances of supervised and weakly-supervised methods in
bold.

Surprisingly, retraining the models using the target taxonomy does not
outperform post-processing using category mapping. However, when a category
mapping is unavailable (as in the “Waseem $\rightarrow$ SBIC” case),
retraining a weakly-supervised classifier using the target taxonomy is the
only option for cross-dataset classification without manually annotating more
data.

An advantage of weakly-supervised methods is that they can utilize unlabeled
documents from the target dataset when they are available. Although
X-ClassAgree still underperforms BERT when both are trained using the source
dataset in the “SBIC $\rightarrow$ Waseem” experiment, it surpasses BERT by 3%
in both accuracy and macro F1 score when using unlabeled target-dataset
documents 121212We can train weakly-supervised models using unlabeled target
dataset, which is equivalent to the in-dataset setting (the X-ClassAgree row
in Table 4)..

Again, X-ClassAgree outperforms X-Class in all cases. Subsequently, we use
X-Class to refer to X-ClassAgree for brevity.

###  4.5 Analysis: What Makes Cross-Dataset Classification Challenging?

As shown in Table 5, X-Class’s performance dropped significantly in the
“Waseem $\rightarrow$ SBIC” cross-dataset setting compared to using the SBIC
training set. In this section, we try to uncover the causes of the performance
drop.

We first plot the per-category F1 score in Figure 1. We can see that the
cross-dataset model achieved comparable performance as the in-dataset model
for the four categories {Jewish, Muslim, Women, Black}. However, it failed in
the two categories {Asian, LGBT}.

Figure 1: Comparing cross-dataset and in-dataset F1 score of X-Class on the
SBIC dataset.

Relevant unlabeled documents. Although the Waseem dataset is labeled using a
more coarse-grained taxonomy, it may contain documents relevant to some (but
not all) fine-grained SBIC categories. Weak supervision usually pseudo-labels
the unlabeled dataset to train a final classifier. Therefore, it will likely
fail when documents related to a particular category are absent in the
unlabeled dataset. We count the frequency of documents containing each
category name in both datasets and present the results in Table 6.

Class |  Seed | Waseem % | SBIC %
---|---|---|---
Sexist |  sexist | 21.83% | 2.70%
Racist |  racist | 0.67% | 1.12%
Women |  women | 11.94% | 9.21%
Black |  black | 0.63% | 22.6%
Jewish |  jewish | 0.51% | 2.00%
LGBT |  gay | 0.59% | 2.95%
Muslim |  muslim | 10.40% | 3.22%
Asian |  asian | 0.08% | 1.71%
Table 6: Frequency of each category name appearing in the Waseem and SBIC
training datasets.

We can observe that the “Asian” category (from the SBIC dataset) is severely
under-represented in the Waseem dataset. The word “Asian” occurs only 4 times,
all in the context of “Asian women/girls”.

Waseem and Hovy (2016) conducted a lexical analysis and showed that their
“Sexist” category is highly skewed towards women, and their “Racist” category
is highly skewed towards Muslims and Jews.131313Although the term “Jewish” has
a low frequency, “Jews” appears in the ten most frequent terms of the “Racist”
category. Coincidentally, these categories also perform the best in the
“Waseem $\rightarrow$ SBIC” setting.

Model | SBIC $\rightarrow$ Waseem | Waseem $\rightarrow$ SBIC
---|---|---
Acc | P/P/F1 |  Acc | P/R/F1
X-Class (src data & src category repr) |  92.6 | 93.4/90.4/91.6 |  69.8 | 62.7/62.2/.58.3
X-Class (src data & tgt category repr) |  93.4 | 92.2/94.0/92.9 |  75.1 | 65.2/55.5/57.8
X-Class (tgt data & tgt category repr) |  96.6 | 97.5/95.2/96.2 |  81.4 | 76.1/85.3/76.6
Table 7: Cross-dataset performance of X-Class using different unlabeled
datasets and category representations.

Category understanding. Jin et al. (2021) argued that weakly-supervised
classification and keyword mining are intrinsically related. The failure to
identify relevant keywords will harm the category representation and, thus,
the classification accuracy. Appendix A presents the full list of keywords
X-Class added to the category representations in both in-dataset and cross-
dataset settings.

A general observation is that X-Class tends to include fewer keywords in its
category representation in the cross-dataset setting. Recall that it stops
adding keywords once the consistency check is violated. We hypothesize that
the mismatch between the dataset and the taxonomy caused the mined keywords to
be noisier and more likely to fail the consistency check.

The four categories that perform the best in both in-dataset and out-dataset
settings also contain better-quality keywords. In contrast, the “Asian”
category’s keyword in the cross-dataset setting is entirely off-topic due to
its rare occurrence and collocation with words like “women” or “girls”. The
“LGBT” category contains many vulgar keywords with sexual references, which
caused it to confuse with the “Women” category.

Class definition vs. dataset. Previous studies tried to explain why HS
classification models generalize poorly across datasets, the most frequently
cited reasons being the lack of a standardized definition of hate speech
(Waseem and Hovy, 2016; Fortuna et al., 2020, 2021) and biased data
distribution (Swamy et al., 2019; Yin and Zubiaga, 2021; Fortuna et al.,
2022). It prompts us to wonder what if we apply the exact class definition to
different datasets or annotate the same dataset using different class
definitions. Unfortunately, manual hate speech annotation is time-consuming
and very challenging. Waseem (2016) and Caselli et al. (2020) are among the
few studies that re-annotated a dataset, providing quantitative analysis or
comparing the models’ performance. However, such studies only apply to a
single dataset. Moreover, the annotation is usually a one-shot effort,
influenced by multiple factors related to the annotation task setup and
knowledge of annotators. There is no way to attribute how much of the
performance drop is due to incompatible class definitions and the data
distribution separately.

In weakly-supervised models, we can interpret the category representation (and
associated keywords) as the class definition. Therefore, the class definition
for the same taxonomy may differ depending on the dataset used to derive the
category representation. Furthermore, we can approximate annotating a dataset
with a different class definition by altering the category representation.

We designed an ablation study to train X-Class models using different
combinations of datasets and class definitions. In Table 7, we present the
results of three configurations in this study:141414All experiments use the
target taxonomy, and all documents are unlabeled. 1) Using source-dataset
documents and category representations derived from the source dataset
(“X-ClassAgree retrain” in Table 5); 2) Using source-dataset documents and
category representations derived from the target dataset; 3) Using target-
dataset documents and category representations derived from the target dataset
(“X-ClassAgree” in Table 4).

X-Class’s cross-dataset performance substantially improved when provided with
the category representation derived from the target dataset.151515Its average
recall in the “Waseem $\rightarrow$ SBIC” experiment decreased sharply mainly
because the category representation for the “Asian” category is far from the
document representation (the Waseem dataset does not contain documents related
to “Asian”). The model did not predict any document as “Asian”. Only one
factor is altered (either the category representation or the unlabeled
training dataset) between the rows in Table 7. Therefore, we can conclude that
the performance difference between rows #1 and #2 is due to different class
definitions, while the performance difference between rows #2 and #3 is due to
different data distributions.

##  5 Conclusions and Future Work

We applied extremely weakly-supervised methods to HS classification. We
analyzed the transferability of HS classification models through comprehensive
in-dataset and cross-dataset experiments and confirmed that weakly-supervised
classification has several advantages over the traditional supervised
classification paradigm. First, we can apply the algorithm across various HS
datasets and domains with taxonomies that cannot be standardized using
category mapping. Second, weakly-supervised models can readily utilize
unlabeled documents in the target domain and do not suffer from domain
mismatch problems. Lastly, weak supervision allows us to “reannotate” a
labeled dataset using a different class definition to facilitate cross-dataset
comparison, which was previously possible only at the cost of expensive manual
annotation.

The presented work is only the beginning of applying weak supervision to HS
detection. We can utilize richer category representations than bag-of-
keywords. However, such representations should be derived in an unsupervised
or weakly-supervised manner to avoid depending on manually labeled datasets. A
promising approach in this direction is Shvets et al. (2021), which extracts
HS targets and aspects relying on open-domain concept extraction.

Lastly, we can study how well the model can generalize to previously unknown
categories, a more challenging task often known as zero-shot classification
(Yin et al., 2019) or open-world classification (Shu et al., 2017).

## Limitations

This study utilizes a monolingual pre-trained language model (PLM) in the
English language (bert-base-uncased). Although the weakly-supervised
classification methods are not limited to a particular language, we have not
explored applying the method to another language. Social media language use
may differ significantly from the data used to train the PLM. Moreover, the
presence of code-switching (Doğruöz et al., 2021) may also degrade a
monolingual PLM’s performance. We explored a RoBERTa checkpoint continually
trained with 60M English tweets (Barbieri et al.,
2020).161616https://huggingface.co/cardiffnlp/twitter-roberta-base However, it
does not yield better performance than BERT. We have not investigated whether
it is due to the training regime or the dataset.

Moreover, in this work, we focus on classifying hate speech (HS)
categories/target groups instead of HS detection (detecting whether a post
contains hate speech or not). To perform hate detection and classification, we
can either combine our method with another HS detection model in a pipeline or
use an adaptation of weakly-supervised text classification incorporating the
“Others” category such as Li et al. (2018) or Li et al. (2021).

Due to limited space, we prioritized in-depth analysis instead of a
comprehensive evaluation. Therefore, we selected only two datasets (and two-
way cross-dataset classification). We are working in parallel on extending
this work to a longer-form journal article to cover more datasets and
experimental results.

Recent work on large language models (LLMs) demonstrated that when the
parameters scale to a certain level, language models exhibit a drastically-
increased performance in zero-shot classification (Zhao et al., 2023). We
reported the performance of a moderately-sized bert-large-uncased zero-shot
model because of limited computational resources and lack of access to
commercial APIs. Larger language models will likely perform much better than
this baseline.

Lastly, understanding HS sometimes requires cultural understanding or
background knowledge. It may be difficult to determine the presence and
category of HS when we take the post out of its context. For example, many
“Sexist” posts in Waseem dataset are tweets related to the Australian TV show
My Kitchen Rules (MKR), and below is a tweet labeled as “Sexist”:

> Everyone else, despite our commentary, has fought hard too. It’s not just
> you, Kat. #mkr

## Ethics Considerations

Although weak supervision requires only unlabeled documents, we demonstrated
that the model might fail when the training dataset does not contain data
related to a particular category or target group. It is especially concerning
because the target groups are often minorities and under-represented.
Therefore, we recommend against “throwing” a weakly-supervised algorithm on a
dataset and hope the model will work. Instead, we should evaluate a model
thoroughly before applying it to the real world, such as manually examining
the model’s predictions, behavioral testing the model using a checklist
(Ribeiro et al., 2020) or conducting unsupervised error estimation (Jin et
al., 2021).

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##  Appendix A Full List of Keywords in X-Class’s Category Representation

Table 9 shows the list of keywords in X-Class’s category representation in the
in-dataset setting (using the unlabeled documents and list of categories from
the same dataset). Table 10 shows the list of keywords in X-Class’s category
representation in the cross-dataset setting (using the unlabeled Waseem
dataset documents to induce category representations of SBIC dataset
categories and vice versa).

##  Appendix B Reproducibility

Table 11 presents the hyper-parameters and their corresponding values to
facilitate reproducing our result.

We use the bert-large-uncased model in HuggingFace as the base pre-trained
language model for the zero-shot PET baseline. PET combines a pattern (or
prompt/instruction) with the input text and prompts the model to predict the
mask token. Unlike open-ended prompting, PET uses a list of hand-crafted
verbalizers (candidate tokens). It classifies documents by assigning the
category whose associated verbalizer receives the highest predicted
probability. PET-style classification is especially beneficial for smaller
PLMs, which do not possess a strong capability of instruction following
(Schick and Schütze, 2021b; Ouyang et al., 2022).

We hand-crafted patterns and verbalizers based on our understanding of the
tasks (without fine-tuning). For Waseem dataset, we use the pattern “<text>
This hate speech is based on <mask>” (verbalizers: gender/race), and for SBIC
dataset “<text> The target group of this hate speech is <mask>” (verbalizers:
women/black/Jews/gay/Muslims/Asian).

##  Appendix C Pseudo-Labeling

Being able to accurately pseudo-label documents is crucial to the success of
weak supervision. We report the accuracy of pseudo-labeling by various weakly-
supervised methods in Table 8.

Dataset (Method) | Acc | P/R/F1
---|---|---
Waseem | 99.1 | 98.4/99.2/98.9
\- WESTCLASS | 77.8 | 77.9/80.1/77.4
\- LOTClass | 64.4 | 72.7/70.9/64.3
SBIC | 93.0 | 89.1/92.8/91.1
\- WESTCLASS | 35.4 | 34.8/35.6/29.9
\- LOTClass | 51.8 | 32.4/26.3/24.5
SBIC $\rightarrow$ Waseem | 91.2 | 92.1/90.9/91.0
Table 8: Pseudo-labeled dataset accuracy calculated against the gold-standard
labels. The default method is X-Class unless otherwise specified. For the
“SBIC $\rightarrow$ Waseem” setting, we use the category mapping in Table 1 to
derive the gold labels. We omit the “Waseem $\rightarrow$ SBIC” setting
because we do not have gold labels.

We can see that the accuracy of pseudo-labeled documents is consistent with
the model’s performance on the test dataset (Table 4). Moreover, LOTClass and
X-Class use the same underlying pre-trained language model (bert-base-uncased)
in their final classifier, while WESTCLASS uses a more traditional
convolutional neural networks architecture (Kim, 2014). The data pseudo-
labeled by X-Class is substantially more accurate than the two baselines in
both datasets. Comparing Table 8 and Table 4, we can observe that the pseudo-
labeling accuracy has a more significant impact on the final classifier’s
accuracy than the model architecture.

We provide randomly sampled pseudo-labeled documents by X-Class in Table 12
(in-dataset) and Table 13 (cross-dataset). In general, the SBIC dataset
contains more diverse and nuanced data. On the other hand, the Waseem dataset
sometimes contains trivial slurs like “… I’m not sexist …”. The samples in the
cross-dataset setting revealed that X-Class tends to wrongly categorize
original “Sexist” posts in the Waseem dataset (which mainly target women) as
“LGBT” and “Asian”.

Class |  Keywords
---|---
Sexist |  sexist sexism misogynist sl*ts sl*t hypocrisy bigotry c*nts hypocrite bigoted pedophile filth c*nt phony barbarity scum bigot genocidal barbaric raping bitchy bigots rapist rapists blasphemy feminists mongering apostacy delusional trashy bimbos a*sholes skank retarded idiotic morons illiterate behead being sexual gays extremists sex islamophobia apostates whining self islamofascists beheads b*tches rape dudes beheading s*cking an enslave pure up common of a sassy vandaliser gender by feminist
Racist |  racist racists racism naziphobia fascist oppression hateful hatred semitic imperialist hating race imperialism genocide inhuman vile ideology violent murderous violence anti nazism vileness brutal propaganda nazis terrorist filthy disgusting radical murdering terrorists hate abuse attacking islamists islamolunatic islamolunatics minority murderers domination jihad terrorism islamist westerners evil killing attack against hated atheists political terror murder culture minorities religious lunatics human conspiracy population hatewatch killings secular religion force cult
Women |  women woman female females girls ladies ch*cks wives men feminist lady girl chick feminists feminine males male gender feminism whores blonde virgins bitches guys hookers prostitutes sl*ts mens wh*re sl*t b*tch p*ssy prostitute virgin couples d*cks breast moms c*nts girlfriend wife sisters dudes attractive sexy betas partners she her beautiful genders lovers normies mothers boys man chads adult couple them fathers mensrights normie assholes they body someone bodies looking v*ginas loser dyk*y sister ones femaloid self mate material raped hooker
Black |  black white colored blacks whites n*gro african negroes negros racial race racist races minorities color africans n*groids minority n*groid racism mixed brown n*ggers skinned blackman slaves peoples ghetto discrimination n*gger people whitey africa red yellow dark savages individuals civil poor disabled blind gorillas savage human folk nonwhite left lynching slavery diversity worthless folks south gorilla majority violent dirty green cotton slave
Jewish |  jewish jews jew synagogue rabbi israel zionist semitic holocaust kosher auschwitz nazi goyim german aryan germans nazis ethiopian germany hitler concentration ash
LGBT |  gay homosexual gays homosexuals homosexuality lesbian lesbians queer transgender homophobic sexuality sexual h*mo queers transgenders masculine sexism sexist trans sex sexually straight dating anal dyke dykes penis marriage rape erection pubic openly pedophile porn nude hiv aids raping interracial relationships relationship genitals boyfriends pedophilia objectifying bi std naked d*ck cocks date misogynist misogyny threesome masturbating shaming stoned v*gina assault bestiality c*nt f*cks rapist genital hot c*ck
Muslim |  muslim muslims islamic islam mosque mosques arabic quran arab muhammad mohammed shia prophet religion terrorists christian religious allah saudi christians terrorist pakistani arabia ali terrorism pakistan prophets bombers isis syria al qaeda banislam radical camels mass bomber bombing church refugees iran suicide iraq middle faith mosul abdul converted jesus akbar military bomb nations militant pray god kkk militia attacks bible propaganda attack
Asian |  asian asians chinese oriental korean japanese american vietnamese indian ethnic mexican americans english latina china eastern foreign exotic european koreans pacific russian north indians spanish russians thai east korea japan country america french cultural western irish countries cuban international nigerian chinaman culture british primitive aged ape inner refugee alien older states europe united animal fat nationality usa russia armed old ignorant special city iq traitor eating animals hungarian food intelligent modern state vietnam rice
Table 9: Full list of keywords in X-Class’s category representation mined from in-dataset setting. Class |  Keywords
---|---
Sexist |  sexist sexism homophobic misogyny misogynist hypocrisy sl*ts sl*t c*nts sl*tty degenerate pedophile pedophilia lesbians sexual masculinity bestiality stereotypical shaming whores feminists masturbating mutilation trashy objectifying homosexuals sexually patriarchy misandry raping c*nt rapist hypocritical gays discriminated genital degeneracy unoriginal a*sholes retarded queers virgins disgusting cannibalism self kinky barbarity promiscuity genitals f*cks rape
Racist |  racist racism racial discrimination race ethnic races blacks black colored white whites n*gro african negroes minority asians minorities oppression diversity negros ghetto n*groid n*groids mixed cultural peoples africans n*ggers color semitic americans american culture asian individuals people savages savage violence slavery n*gger mixing transgenders mass skinned worthless queer slaves
Women |  women woman female females ladies girls feminine feminist feminists feminism womens gender male men girl lady ch*cks blonde blondes males femininity mens wives guys ch*ck wife yesallwomen b*tches daughters her she stars b*tchy girlfriend body b*tch sister feminismisawful announcers promogirls sportscasters bodies models they themselves refs ones them couples someone diva their sjw mother
Black |  black white blacks whites racists racist race minorities minority racism africans oppressed americans oppression people population human
Jewish |  jewish jews jew judaism israel palestinian zionist palestinians israelis israeli palestine semitic semitism hamas gaza holocaust nazis nazi egyptians
LGBT |  gay gays sexual sex sexism sexists sexist rape raping misogynist rapists reproductive misogyny pedophile rapist genitals sl*ts sl*t raped c*nts assault dudes masculinity porn boys shaming c*nt hypocrisy v*gina bigotry rapes bigoted hypocrites hateful haters stereotype openly bimbos wh*re abuse misandrist
Muslim |  muslim muslims islamic islam islamist sunni religious islamists jihadi jihadis arab arabs mosques shia quran jihad religion muhammad mohammed taqiyya allah terrorist terrorists prophet believers religions christian hadiths sharia baghdadi secular caliphate hadith saudis saudi pakistani imam christians terrorism islamolunatics isis islamofascists arabian arabia umar extremists hindus pakistan taquiyya medina qurans mullah sunnah westerners
Asian |  asian intelligent attractive ignorant young pretty dumb hot rich fat ugly stupid smart tough looking crazy insane blond selfish common brainwashed correct biased clever annoying childish being most hating seeing old beautiful terrible killer self innocent a everydaysexism friendly average ridiculous idiotic extremely poor good bad flawed decent great low simple nice an legit out safe trash doing useless awful corrupt funny sick strong other known working many making best no
Table 10: Full list of keywords in X-Class’s category representation mined from cross-dataset setting. Hyper-parameter |  Value |  Description
---|---|---
random_seed |  42 |  The fixed random seed. Used to split the dataset and initialize parameters.
lm_ckp |  bert-base-uncased |  The pre-trained language model checkpoint used to derive document representations.
clf_ckp |  bert-base-uncased |  The pre-trained language model checkpoint used to fine-tune the final classifier. Used in both supervised and weakly-supervised settings.
min_freq |  5 |  Minimum frequency of a word to be included in the vocabulary.
T |  100 |  Maximum terms to include in the category representation.
cluster_method |  gmm |  Method to perform document class alignment in X-Class. We use the default Gaussian Mixture Model with tied covariance.
pca_dim |  64 |  Dimension of principal component analysis before performing clustering.
conf_threshold |  0.5 |  The percentage of most confident documents assigned by GMM to include in the pseudo-labeled training set.
max_len∗ |  64 |  The maximum number of tokens of the input posts. Input longer than it will be truncated.
batch_size‡ |  64 |  The training batch size.
n_epochs‡ |  6 |  The number of training epochs.
learning_rate‡ |  2e-5 |
weight_decay‡ |  0.05 |
lr_schedule† |  cosine w/ warmup |  Learning rate schedule. Cosine learning rate schedule with 1/3 steps warm-up.
Table 11: Full list of hyper-parameters. The first block is hyper-parameters related to X-Class. The second block is hyper-parameters related to the classifier fine-tuning. * denotes values set based on data analysis. ‡ denotes values set by manual hyperparameter tuning. † denotes values chosen based on our experience but are not tuned. All the rest of the parameters default from the X-Class repo. Class |  Posts | Correct?
---|---|---
Sexist |  AbuserDynamics Girls should count themselves lucky if they got a guy that still shouts when his mad, means he cares | $\checkmark$
|  I have a stupid man crush on Lloyd and I can’t help it. mkr2015 mkr sexylloydmkr | $\checkmark$
|  RT This lady is a terrible official. Not because she’s a lady though. I’m not sexist. But because she’s a stupid b*tch. Cavs | $\checkmark$
Racist |  RT CNN reports on the murders of homosexuals by the Islamic State, but the problem isn t just ISIS, it s Islam | $\checkmark$
|  Yes, there is even more rape in Muslim countries but it is not reported. | $\checkmark$
|  For example, the Quran says leaving Islam is not accepted. The Hadiths say kill anyone who leaves. | $\checkmark$
Women |  Molly make b*tches crazy | $\checkmark$
|  The emoji I use when I’m flirting wit b*tches gt | $\checkmark$
|  Rule number two don’t trust nobody especially a b*tch with a hookers body | $\checkmark$
Black |  It’s funny when u get a black female mad they start acting ghetto then a b*tch | $\checkmark$
|  White bitches be lovin da colored men | ✗
|  Those ratchet broads don’t represent me but YOUR white a*s can’t talk trash about them ’cause you on racist sh*t “Black”’ | $\checkmark$
Jewish |  What was the Jewish pirate’s nickname? Hooknose. | $\checkmark$
|  Do you know why do Jewish kids wait behind the chimney? Because they’re waiting for their parents . | $\checkmark$
|  Why do Jews hate a*sholes? The can’t stand gas chambers. | $\checkmark$
LGBT |  Any man even considering plastic surgery is gay gay gay imagine ya boy say “I’d get more hoes then u after I get these new lips” | $\checkmark$
|  do it i f*cking dare you f*ggot | $\checkmark$
|  What do you call a gay drive by? A fruit roll up. | $\checkmark$
Muslim |  RT Fuck Islam you pigfucking trash troll. | $\checkmark$
|  Bomb your house, Then f*ck your b*tch. All hail Al Qaeda | $\checkmark$
|  What did the Muslim say after eating a bacon sandwich? That’s the way Allah Allah I like it. | $\checkmark$
Asian |  My economics professor just called these asians retards | $\checkmark$
|  Korean idols are perfectly handsome, cute, funny, stylish, hot, know how to dance, have a wonderful voices. | ✗
|  The Stock Exchange I like the NYSE just like the Ethiopian population count. Going down faster than ever. | ✗
Table 12: Randomly sampled pseudo-labeled examples for each category in the in-dataset setting. Class |  Posts | Correct?
---|---|---
Sexist |  on sale a*s hoes | $\checkmark$
|  Molly make b*tches crazy | $\checkmark$
|  This n*ggga said I be branding b*tches | ✗
Racist |  RT Wow the stupid n*gger in LeBron really came out there | $\checkmark$
|  My Moor friends,no not black friends,but Moor friends said N*gger came from Nigeria…You are so lost..Stop tagging me… | $\checkmark$
|  RT Remember the “yellow badge” Nazis used? Israel is making Muslim women carry a yellow badge order to pray in Al Aqsa. h | $\checkmark$
Women |  RT I’m no sexist but the last thing I wanna read about is women’s, football or cricket on the sky sports news app! controve | $\checkmark$
|  RT Then I guess Feminism is just a sideshow as much as WWE wrestling in general.. Irony is off the c | $\checkmark$
|  Are you even a real person? I’m not sexist. But Men are superior to women | $\checkmark$
Black |  Can’t forget it…never heard about it… | ✗
|  …with a flat face. The nose a bay window. | ✗
|  But look at the reality disconnect. Burak says he is for freedom and against all slavery while at the … | $\checkmark$
Jewish |  Max Blumenthal is bad mouthing you. Not enough room at the top for all the self genocidal Jews. Israel Palestine | $\checkmark$
|  The job Mohammed set Muslims is not done while Israel exists. | $\checkmark$
|  The Jews of Europe should just come to the US. Then the Europeans can allow Islam to take them backwards. | $\checkmark$
LGBT |  RT I’m not sexist but right now I hate girls !!!! | ✗
|  RT This is not sexist but I want to punch both of the girls from broad city workaholics | ✗
|  RT This is why girls don’t play football. Someone’s feelings get hurt and boom, it’s out of hand. Go ahead and call me sexist, | ✗
Muslim |  You didn’t recognize the irony of me using your method because you are an ignorant Muslim. | $\checkmark$
|  And you lie again. The majority of Muslims were forced into it. | $\checkmark$
|  RT Arab slave trade 140 to 200 million non Muslim slaves from all colors and nationalities still happening today! | $\checkmark$
Asian |  Someone really needs to get the sniffer dogs onto Kat offherlips MKR | ✗
|  MKR anyone can cook from a can girls. | ✗
|  Kat you don’t look suspicious at all! MKR | ✗
Table 13: Randomly sampled pseudo-labeled examples for each category in the
cross-dataset setting.