SlowGuess/NAACL_2021_OCR huggingface dataset:
Ayush Kaushal and Avirup Saha and Niloy Ganguly
{ayushk4, saha.avirup}@gmail.com, niloy@cse.iitkgp.ac.in
The stance detection task aims at detecting the stance of a tweet or a text for a target. These targets can be named entities or free-form sentences (claims). Though the task involves reasoning of the tweet with respect to a target, we find that it is possible to achieve high accuracy on several publicly available Twitter stance detection datasets without looking at the target sentence. Specifically, a simple tweet classification model achieved human-level performance on the WT-WT dataset and more than two-third accuracy on various other datasets. We investigate the existence of biases in such datasets to find the potential spurious correlations of sentiment-stance relations and lexical choice associated with the stance category. Furthermore, we propose a new large dataset free of such biases and demonstrate its aptness on the existing stance detection systems. Our empirical findings show much scope for research on the stance detection task and proposes several considerations for creating future stance detection datasets. $^{1}$
Stance detection is a vital sub-task for fake news detection (Pomerleau and Rao, 2017), automated fact checking (Vlachos and Riedel, 2014; Ferreira and Vlachos, 2016), social media analysis (Zhang et al., 2017), analyzing online debates (Bar-Haim et al., 2017) and rumour verification (Derczynski et al., 2017; Gorrell et al., 2019). Furthermore, it is also an essential measure for progress in Natural Language Understanding, especially in the nois-text domain.
Over the recent years, several stance detection datasets have been proposed. These datasets, in turn, facilitated progress in stance detection research, with some systems achieving up to  $93.7\%$  accuracy (Dulhanty et al., 2019). However, most
Figure 1: An example tweet from WT-WT dataset with different targets.
of these state-of-the-art systems are complex deep neural networks, making them difficult to interpret. Lack of explainability raises concern since previous works (Gururangan et al., 2018; Goyal et al., 2017; Cirik et al., 2018; Geva et al., 2019) on other tasks demonstrated that superficial dataset biases could result in inflated test-set performance. With this motivation, we carry out the first study analyzing several publicly available Twitter stance detection datasets. Our experiments reveal rampant biases in datasets through which even target-oblivious models can achieve impressive performance.
Various existing works have hinted at the presence of such dataset biases. For example, TAN model (Du et al., 2017) is a very competitive stance detection model. However, Ghosh et al. (2019) recently proved that TAN does not take advantage of target information at all. In RumourEval-2017 (Derczynski et al., 2017), models delivered up to 0.74 accuracy without any knowledge of the target, being only short of 0.004 from the best model considering the context. Similarly, in RumourEval-2019 (Gorrell et al., 2019), the runner-up model (Fajcik et al., 2019) observed a 0.43 decrease in accuracy by considering the target information. Schiller et al. (2020) discovered that stance detection models are prone to adversarial attacks of paraphrasing, spelling error and negation similar to other NLP tasks (Ribeiro et al., 2020). However,
$^{1}$ Code: https://github.com/Ayushk4/bias-stance Dataset: https://github.com/Ayushk4/stance-dataset
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3879-3889 June 6-11, 2021. ©2021 Association for Computational Linguistics
Dataset	size	Target	# Stance	Tweets	DT/T
Number	Domain	Type	Unique	Scrapped
WT-WT	51284	5	Finance (M&A)	fixed	4	50210	45865	2%
SE16	4162	5	Various	fixed	3	4162	-	0%
M-T	4455	3	Political	fixed (pairs)	3	4413	2688	0.9%
RE17	5568	-	Rumour-claims	free-form	4	556k	-	0%
RE19	8574	-	Rumour-claims	free-form	4	8574	-	0%
Encryption	2999	1	Encryption-debate	fixed	3	2522	1634	0%
Table 1: Statistics of the Twitter stance detection datasets considered.
this is the first work providing a detailed insight into the alarmingly impressive performance of target-oblivious models.
Target plays a crucial role in deciding stance. Consider the example in Figure 1. Here, the tweet stance varies for the two targets. The existing datasets have very few examples with different target labels. Models can pick up on pseudo signals in the tweet content and shortcut the task without looking at the targets. These signals or biases occur due to inherent biases in our language and human nature. For example, certain lexical choices can correlate with their respective stance classes. Upon discovering and studying such correlations, we augment the WT-WT dataset addressing these issues and re-evaluate the stance detection systems.
We make the following contributions. We empirically demonstrate biases across a variety of Twitter stance detection datasets and carry out a detailed analysis of these datasets. Consequently, we propose a new large scale dataset free of such spurious cues and re-evaluate the stance detection systems to show the usefulness of this dataset.
2 Biases in Stance Detection Datasets
We first discuss the datasets considered (Section §2.1), followed by our experiments (Section §2.2) and analysis (Section §2.3).
2.1 Datasets Considered
We consider a wide variety of publicly available Twitter stance detection datasets including cross-target, multi-target, rumour-claim variants of stance detection. These datasets have a diverse set of targets ranging from free-form sentences to fixed target entities.
Over the past few years, several more variants of this task have been proposed, such as in non-English language (Darwish et al., 2017; Kucuk and Can, 2018; Lai et al., 2018) and multi-lingual settings (Zotova et al., 2020; Vamvas and Sennrich, 2020), different learning paradigms of unsu
pervised (Darwish et al., 2019) semi-supervised (Mohammad et al., 2016b), zero-shot (Allaway and McKeown, 2020) and non-Twitter tasks of debate-argument stance (Bar-Haim et al., 2017) and headline-body stance detection (Pomerleau and Rao, 2017).
Here, however, we only study the English Twitter stance detection tasks in fully supervised learning settings. Specifically, we consider 6 datasets - WT-WT (Conforti et al., 2020), SE16 (task-A) (Mohammad et al., 2016b,a) M-T (Sobhani et al., 2017), RE17 (Derczynski et al., 2017), RE19 (Gorrell et al., 2019) and Encryption (Addawood et al., 2017) with their statistics mentioned in Table 7. This table also reports the percentage of tweets in the entire dataset labelled for different targets (DT) given by  $DT / T$  in the last column. We can see that these datasets have very few tweets annotated for different targets. The M-T dataset's targets are a pair of politicians, and for each of its tweet-targets, the label is a pair of stances. We formulate detecting these two stance-pair as separate tasks for the experiments in the following section.
2.2 Performance of Target-Oblivious Models
Method: Given a tuple (tweet, target, stance), a target-oblivious classifier  $f(tweet) \rightarrow$  stance is trained in a supervised setting. It is expected that such a classifier would generalize poorly for an unbiased dataset. We set this target-oblivious classifier as the standard Bert classifier (Devlin et al., 2019) pre-trained on Tweets (Nguyen et al., 2020). It receives the input "[CLS] tweet [SEP)". Additionally, we train a strong target-aware Bert classifier model for stance detection (Ghosh et al., 2019). This model takes input "[CLS] tweet [SEP] target [SEP)". We use PyTorch (Paszke et al., 2019), HuggingFace (Wolf et al., 2019), Wandb (Biewald, 2020) and Scikit-Learn (Pedregosa et al., 2011) for our experiments. We use Adam optimizer (Kingma and Ba, 2014). We elaborate the full experimental settings in the appendix §A.
Models	F1Macro across healthcare merger operations	avgF1	avgwF1	Entertainment
CVS_AET	CI_ESRX	ANTM_CI	AET_HUM	F1Macro
Bert (no-target)	0.673	0.703	0.745	0.759	0.720	0.720	0.347
Human Upperbound	0.753	0.712	0.744	0.737	0.736	0.743	N/A
Bert (with target)	0.668	0.709	0.746	0.756	0.720	0.719	0.433
Random guessing	0.222	0.237	0.231	0.236	0.230	0.232	0.201
Majority guessing	0.162	0.139	0.155	0.134	0.151	0.148	0.161
Table 2: Results on WT-WT dataset (Conforti et al., 2020). Values higher than Human Upperbound are boldfaced.
Model	Acc	F1Wtd	F1Macro
SemEval 2016 (SE16)
Bert (no target)	0.708	0.711	0.675
Bert (target)	0.738	0.737	0.695
Majority Class	0.572	0.416	0.243
Random	0.333	0.353	0.313
M-T Stance Dataset
Bert (no target)	0.675	0.673	0.654
Bert (target)	0.691	0.681	0.657
Majority Class	0.419	0.247	0.197
Random	0.333	0.336	0.331
RumourEval 2017 (RE17)
Bert (no target)	0.783	0.766	0.543
Bert (target)	0.769	0.760	0.543
Majority Class	0.742	0.632	0.213
Random	0.25	0.310	0.189
RumourEval 2019 (RE19)
Bert (no target)	0.840	0.821	0.577
Bert (target)	0.836	0.829	0.604
Majority Class	0.808	0.722	0.223
Random	0.25	0.329	0.171
Encryption Debate
Bert (no target)	0.916	0.903	0.778
Bert (target)	0.907	0.894	0.755
Majority Class	0.863	0.801	0.464
Random	0.500	0.576	0.424
Results and Discussion: The WT-WT dataset is a cross-target dataset containing four in-domain (healthcare) and one out-of-domain (entertainment) target. For the in-domain evaluation, training is done on three health mergers while testing is done on the fourth unseen target. For out of domain, training is on all four health mergers and testing on the entertainment domain. Table 2 shows the performance of target-oblivious Bert, target-aware Bert and the human upper-bound. The human expert upper-bound values were taken from the WT-WT dataset. We observe that the target-oblivious model consistently performs very close to the target-aware model for all the targets. Both these models achieve near-human performance overall on in-domain targets. The target-oblivious Bert surpasses human upper-bound for two mergers individually. Such a feat is alarming, especially because cross-target stance is a more challenging variant (Küçük and Can, 2020; Wang et al., 2020) of the task.
Table 3: Results on other stance detection datasets.
Stance	Lexicons
Support	approves (3.3%), approve (5.1%), billion (26.2%), shareholder (0.7%), close (6.4%)
Refute	urges (3.0%), blocked (5.5%), sues (4.3%), blocks (4.8%), block (21.8%)
Comment	ceo (3.7%), healthcare (11.8%), mean (2.3%), merger (29.3%), trial (3.4%)
Unrelated	stocks (3.4%), size (2.6%), merge (11.3%), bid (19.0%), agreement (16.7%)
Table 4: Top 5 stance-wise words in WT-WT dataset by PMI(word, stance) across health domain tweets along with percentage of the respective stance-class labelled tweets having each word.
Results on the other datasets are shown in Table 3. We compare these results with random guessing, predicting majority class and the target-aware Bert. Additionally, RE17, R19, and Encryption datasets are heavily skewed datasets, so Macro-F1 is the proposed metric (Gorrell et al., 2019).
The target-oblivious Bert delivers more than two-third classification accuracy consistently across all these datasets. This model achieves impressive performance for all metrics in SE16 and M-T datasets, while also performing significantly above majority class for datasets with skewed distributions on the Macro-F1 metric. The performance delivered by target oblivious Bert is also very close to the target-aware Bert model on every metric. These surprising numbers across all the datasets indicates the presence of spurious cues that encourages the models to bypass the need for looking at the target.
2.3 Dataset Analysis
After the finding from our previous section, we sought to discover the form in which spurious cues exist and use those findings to create a new dataset. We mainly consider the largest and most recent dataset, WT-WT for analysis. We first discuss target-independent lexical choices associated with stance, followed by target-independent sentiment-stance correlations.
Stance and tweet lexicons: We calculate the smoothed Point-wise Mutual Information (PMI)
Figure 2: Procedure to create tWT-WT dataset from WT-WT.
between tweet and stance following the exact same procedure as (Gururangan et al., 2018) after removing stopwords. Table 4 shows that top 5 stance-wise words along with the fraction of tweets containing those words. We observer that certain groups of target-independent lexicons are highly correlated with stances in some cases occurring in more  $29\%$  of the tweet. For Support and Refute classes respectively, we find the co-occurrence of indicative words for the status of merger, such as 'approves' or 'blocks'. The Comments relating to these health companies' mergers often talk about its impact, leading to the choice of lexicons containing words like 'healthcare' and 'mean' with this stance. Similarly, Unrelated tweets often talk about things related to the companies but unrelated to the merger operation such as 'stocks' or 'bids'.
Sentiment-stance correlation: Stance detection differs from the sentiment analysis task (Mohammad et al., 2016b). However, we observe a strong correlation of sentiment with stance. Formally, we obtain a sentiment score between 0 (negative) and 1 (positive) for each tweet using XLNet model (Yang et al., 2019) trained on SST (Socher et al., 2013; Pang and Lee, 2005) and Imdb (Maas et al., 2011). The average sentiment scores of these tweets across Support, Refute, Comment and Unrelated stances were found to be 0.237, 0.657, 0.492 and 0.485 respectively, while their variance were 0.087, 0.056, 0.110, 0.108. The tweets with Support and Refute stance have strong negative or positive sentiment on average while for the other two is it neutral on average but having a high variance. These serve as strong evidences for stance-sentiment correlations.
Thus sentiment and lexicons together are some of the spurious cues in WT-WT dataset. We found
such cues in the remaining datasets, varying with their domains. For example, RE19 has a question mark in more than  $75\%$  'query' stance tweets, while it is present only in  $11\%$  of the entire remaining dataset. Similarly  $75\%$  of tweets with 'deny' stance have highly negative sentiment of less than 0.1 score. In SE16 dataset, had  $91.4\%$  of tweets without any opinion² had 'None' stance despite the stance detection task being different from opinion mining task (Mohammad et al., 2016b).
3 The Targeted WT-WT (tWT-WT) dataset
With the understanding from the previous section, we propose a new stance detection dataset on which target-unaware models will not perform well. We use the following reasoning for creating the new dataset. If a tweet in the dataset has different stances depending on different targets, then simple tweet classification models will not be able to perform well. Thus we attempt to increase DT/T ratio from Table 7. Formally, we take the WT-WT dataset, which is the largest dataset of its kind, with high-quality experts labels of 0.88 Cohen-  $\kappa$  (Cohen, 1960), and generate new (tweet, target, stance) triplets in three ways.
First, we attempt to remove the sentiment-stance correlation by making the stance-wise average sentiment neutral. The WT-WT dataset has 5 targets, one target for each merger. We introduce 5 new additional targets which are negations of the original ones. Formally, if the tweet has a Support (Refute) stance to the target  $CVS\_ AET$ , then its stance to the negated target  $NEG\_CVS\_AET$  will be inverted to Refute (Support). This is done only
2Tweets have gold labels for opinion-class in the dataset.
Models	F1Macro across healthcare merger operations	avgwF1	avgF1	Entertainment
CVS_AET	CI_ESRX	ANTM_CI	AET_HUM	F1Macro
Bert (no-target)	0.161	0.258	0.297	0.340	0.264	0.260	0.163
Bert (with target)	0.460	0.386	0.596	0.598	0.510	0.527	0.365
SiamNet	0.293	0.292	0.273	0.398	0.312	0.310	0.150
TAN	0.170	0.222	0.308	0.332	0.258	0.260	0.150
Random Guessing	0.233	0.206	0.225	0.223	0.222	0.225	0.205
Majority Guessing	0.145	0.198	0.181	0.177	0.175	0.171	0.169
Table 5: Results on tWT-WT dataset in the same cross-target settings as given by Conforti et al. (2020).
for the two stance classes with non-neutral average sentiment score. Introducing such negated targets reduces their sentiment to near neutral.
Second, we remove lexicon-stance correlations by creating multiple targets with different stances for each tweet. Formally, for each tweet  $t$  with only one labelled target  $tgt$ , if the tweet-target pair  $(t, tgt)$  has the stance  $\neq$  'Unrelated', then pick a target  $tgt'$  where  $tgt' \neq tgt$  and add the tuple  $(t, tgt', Unrelated)$  to the dataset. Due to WT-WT data collection and annotation procedure, this will not generate any wrong labels. This augmentation reduces the lexicon-stance correlations, by having similar sets of lexicons introduced for different stances. Hence, it guarantees target-oblivious shortcuts to result in poor performance.
Last, we balance the target-wise class-distributions. For the tuples with 'Comment' and 'Unrelated' stances, we create a new tuple with inverted target (same as the first step) for  $50\%$  and  $75\%$  such examples randomly.
The resulting dataset contains 111596 tweet-target pairs each belonging to a stance class. Each merger has at least 10000 data points. The class distribution is also somewhat balanced with more than 10k examples for the least occurring class. Among the tweet-target pairs, the pairs classified as Support, Refute, Comment and Unrelated are distributed in the ratio 1:1:3:5 approximately, having a similar distribution to the WT-WT dataset.
3.1 Re-evaluating stance detection systems
We propose a similar cross-target evaluation setting for tWT-WT as WT-WT. For the in domain (health) mergers, we train on three health merger (total six targets including negated target for each merger) and test on the fourth health merger. For the out-of-domain evaluation, we train on the eight targets corresponding to the 4 health mergers and test on the two targets for entertainment merger.
We re-evaluate the existing stance detection models on tWT-WT dataset. We consider Bert (with
target), target-oblivious Bert from §2.2, along with the two strongest baselines from the WT-WT paper - SiamNet (Santosh et al., 2019) and TAN (Du et al., 2017). For SiamNet and TAN models, we replace the Glove (Pennington et al., 2014) and LSTM (Hochreiter and Schmidhuber, 1997) features with better features from Bert.
Table 5 shows the performance of these models. Bert (no-target) gives very low performance, showing that target oblivious models perform poorly on this dataset. Similarly, TAN which has been proven to not at take advantage of the target information (Ghosh et al., 2019) also performs very poorly on the dataset. The target aware Bert offers a competitive performance still being only at 0.51 F1 score. SiamNet follows next at 0.31 F1 score. Both these models have their performance reduced significantly from WT-WT dataset.
In this paper we demonstrated the presence of biases across several Twitter stance detection datasets, which aid simple tweet classifiers to achieve impressive performance. We carried out an investigation for presence of bias for the WT-WT dataset and found correlations of stance-class with sentiment and lexical choice. Consequently, we proposed a new bias-free stance detection dataset -tWT-WT, the largest of its kind. Evaluation of our baselines on this new dataset demonstrates scope for future research on stance detection. The observations are also crucial for the creation of new stance detection datasets. Our future work includes analysing multilingual datasets and exploring explainable target aware stance detection models.
We would like to thank the anonymous reviewers for their valuable feedback and suggestions. We also thank the Computer Science and Engineering Department at the IIT Kharagpur for providing us with the compute facilities for our research.
Aseel Addawood, Jodi Schneider, and Masooda Bashir. 2017. Stance classification of twitter debates: The encryption debate as a use case. In Proceedings of the 8th International Conference on Social Media & Society, #SMSociety17, New York, NY, USA. Association for Computing Machinery.
Emily Allaway and Kathleen McKeown. 2020. Zero-Shot Stance Detection: A Dataset and Model using Generalized Topic Representations. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 8913-8931, Online. Association for Computational Linguistics.
Roy Bar-Haim, Indrajit Bhattacharya, Francesco Dinuzzo, Amrita Saha, and Noam Slonim. 2017. Stance classification of context-dependent claims. In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers, pages 251-261, Valencia, Spain. Association for Computational Linguistics.
Christos Baziotis, Nikos Pelekis, and Christos Doukkeridis. 2017. DataStories at SemEval-2017 task 4: Deep LSTM with attention for message-level and topic-based sentiment analysis. In Proceedings of the 11th International Workshop on Semantic Evaluation (SemEval-2017), pages 747-754, Vancouver, Canada. Association for Computational Linguistics.
Iz Beltagy, Kyle Lo, and Arman Cohan. 2019. SciBERT: A pretrained language model for scientific text. 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 3615-3620, Hong Kong, China. Association for Computational Linguistics.
Lukas Biewald. 2020. Experiment tracking with weights and biases. Software available from wandb.com.
Jane Bromley, Isabelle Guyon, Yann LeCun, Eduard Säckinger, and Roopak Shah. 1993. Signature verification using a "siamese" time delay neural network. In Proceedings of the 6th International Conference on Neural Information Processing Systems, NIPS'93, page 737-744, San Francisco, CA, USA. Morgan Kaufmann Publishers Inc.
Volkan Cirik, Louis-Philippe Morency, and Taylor Berg-Kirkpatrick. 2018. Visual referring expression recognition: What do systems actually learn? In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers), pages 781-787, New Orleans, Louisiana. Association for Computational Linguistics.