| { | |
| "paper_id": "2021", | |
| "header": { | |
| "generated_with": "S2ORC 1.0.0", | |
| "date_generated": "2023-01-19T03:35:38.055494Z" | |
| }, | |
| "title": "Adversarial Training for News Stance Detection: Leveraging Signals from a Multi-Genre Corpus", | |
| "authors": [ | |
| { | |
| "first": "Costanza", | |
| "middle": [], | |
| "last": "Conforti", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Language Technology Lab", | |
| "institution": "University of Cambridge", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Jakob", | |
| "middle": [], | |
| "last": "Berndt", | |
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| "institution": "University of Cambridge", | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Mohammad", | |
| "middle": [ | |
| "Taher" | |
| ], | |
| "last": "Pilehvar", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Language Technology Lab", | |
| "institution": "University of Cambridge", | |
| "location": {} | |
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| "email": "" | |
| }, | |
| { | |
| "first": "Marco", | |
| "middle": [], | |
| "last": "Basaldella", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Language Technology Lab", | |
| "institution": "University of Cambridge", | |
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| { | |
| "first": "Chryssi", | |
| "middle": [], | |
| "last": "Giannitsarou", | |
| "suffix": "", | |
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| "laboratory": "", | |
| "institution": "University of Cambridge", | |
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| }, | |
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| }, | |
| { | |
| "first": "Flavio", | |
| "middle": [], | |
| "last": "Toxvaerd", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "", | |
| "institution": "University of Cambridge", | |
| "location": {} | |
| }, | |
| "email": "" | |
| }, | |
| { | |
| "first": "Nigel", | |
| "middle": [], | |
| "last": "Collier", | |
| "suffix": "", | |
| "affiliation": { | |
| "laboratory": "Language Technology Lab", | |
| "institution": "University of Cambridge", | |
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| "year": "", | |
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| "abstract": "Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.", | |
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| "abstract": [ | |
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| "text": "Cross-target generalization constitutes an important issue for news Stance Detection (SD). In this short paper, we investigate adversarial cross-genre SD, where knowledge from annotated user-generated data is leveraged to improve news SD on targets unseen during training. We implement a BERT-based adversarial network and show experimental performance improvements over a set of strong baselines. Given the abundance of user-generated data, which are considerably less expensive to retrieve and annotate than news articles, this constitutes a promising research direction.", | |
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| "text": "Stance Detection (SD) is an important NLP task (Mohammad et al., 2017) with widespread applications, ranging from rumor verification (Derczynski et al., 2017) and fact checking (Hanselowski et al., 2019) . Traditionally, research in SD focused on user-generated data, such as Twitter or Reddit (Gorrell et al., 2019) : this is mainly due to the abundance of such data, which are usually freely available online; moreover, user-generated data tend to be relatively short and compact, and thus more affordable to annotate and process. Starting from popular shared tasks such as Pomerleau and Rao (2017) , SD on complex and articulated input, such as news articles, has gained increasing popularity. Notably, effective news SD would constitute an invaluable tool to enhance the performance of human journalists in rumor and fake news debunking (Thorne and Vlachos, 2018) .", | |
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| "start": 47, | |
| "end": 70, | |
| "text": "(Mohammad et al., 2017)", | |
| "ref_id": "BIBREF14" | |
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| { | |
| "start": 133, | |
| "end": 158, | |
| "text": "(Derczynski et al., 2017)", | |
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| { | |
| "start": 177, | |
| "end": 203, | |
| "text": "(Hanselowski et al., 2019)", | |
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| "start": 294, | |
| "end": 316, | |
| "text": "(Gorrell et al., 2019)", | |
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| "start": 576, | |
| "end": 600, | |
| "text": "Pomerleau and Rao (2017)", | |
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| "start": 841, | |
| "end": 867, | |
| "text": "(Thorne and Vlachos, 2018)", | |
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| "section": "Introduction", | |
| "sec_num": "1" | |
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| "text": "In line with the general trend in NLP, deep learning-based models have long since established state-of-the-art results in news SD (Hanselowski et al., 2018) . Notably, training neural networks relies heavily on the availability of large labeled datasets, which are especially expensive to obtain for items such as news articles. As a consequence, following research on other text classification tasks such as sentiment analysis (Du et al., 2020) , research in SD investigated effective methods for cross-domain SD, where the scarcity of data for a specific dataset is supplemented with stance-annotated data from other domains. In this context, preliminary research in adversarial domain adaptation obtained promising results for both Twitter and news (Xu et al., 2019) SD.", | |
| "cite_spans": [ | |
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| "start": 428, | |
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| "text": "(Du et al., 2020)", | |
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| "text": "(Xu et al., 2019)", | |
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| "section": "Introduction", | |
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| "text": "In this paper, we focus on the new task of crossgenre SD: we consider adversarial knowledge transfer from two datasets, WT-WT and STANDER, which collect samples in the same domain (i.e. the financial domain), but which belong to different genres (i.e. Twitter and news). We show experimentally that improvements in news SD performance can be achieved through cross-genre SD, which constitutes a promising direction for future research.", | |
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| "section": "Introduction", | |
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| "text": "In this work, we rely on two recently released datasets for news and Twitter SD: the STANDER corpus for the news genre (Conforti et al., 2020a) , and the WT-WT corpus for Twitter (Conforti et al., 2020b) . Both corpora collect samples discussing four mergers and acquisition (M&A) operations in the healthcare industry (Table 2) : an M&A operation, or merger, is the process in which a company (the buyer) attempts to acquire the ownership of another company (the target). A merger succeeds if ownership of the target is transferred, but can fail at any stage of discussions or can be blocked by authorities due to, e.g., antitrust concerns (Bruner and Perella, 2004) . Table 1 : Label distribution in the STANDER News SD corpus and in the WT-WT Twitter SD corpus.", | |
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| "text": "(Conforti et al., 2020a)", | |
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| "text": "(Bruner and Perella, 2004)", | |
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| "text": "(Table 2)", | |
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| "text": "Table 1", | |
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| "section": "An Aligned Multi-Genre Stance Detection Corpus", | |
| "sec_num": "2" | |
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| { | |
| "text": "Samples in both STANDER and WT-WT are manually stance-labeled by domain experts using a four-classes annotation schema distinguishing between support, refute, comment and unrelated, which expresses the sample's orientation about the outcome of the M&A (succeeded or rejected).", | |
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| "text": "As observed in Conforti et al. (2020a) , the two corpora present comparable signals, but display different characteristics which reflect the diverse genres they belong to. The Twitter samples are abundant and noisy, as indicated by the high percentage of unrelated and commenting samples ( Figure 1 and Table 1 ). On the other hand, STANDER collects considerably fewer samples, which are substantially longer and articulated; moreover, news articles in STANDER have been published in high-reputation outlets after careful editorial review, and thus contain a more formal and orthographically correct language with respect to user-generated tweets.", | |
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| "section": "An Aligned Multi-Genre Stance Detection Corpus", | |
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| "text": "Given the scarcity of news articles in STANDER, which are around one order of magnitude less abundant than tweets in WT-WT, and considering that both corpora collect the same targets in the same domain, cross-genre SD from WT-WT to STANDER seems to constitute an interesting research direction. However, due to the consistent genre differences, transferring knowledge from WT-WT to STANDER is non-trivial. To allow the model to capture the stance-specific features from the WT-WT samples which are useful to perform news SD, while ignoring the Twitter-specific features, we propose to treat the task adversarially.", | |
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| "section": "Motivation", | |
| "sec_num": "3.1" | |
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| "text": "We propose to consider two classification problems -SD and genre identification (GI) -with a shared BERT-based feature extractor, as shown in Figure 2 . To derive genre-invariant features, the GI component is trained adversarially. The model receives an input sample as:", | |
| "cite_spans": [], | |
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| { | |
| "start": 142, | |
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| "text": "Figure 2", | |
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| "section": "Models", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "[CLS] Target [SEP] Text [SEP]", | |
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| "sec_num": "3.2" | |
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| "text": ", where Target is the SD target, expressed as the sentence \"A (a) will merge with B (b)\" (where upper-and lowercase a and b refers resp. to the buyer's and the target's company names and acronyms); for tweets, Text is the entire sample's text, while for news, we concatenate the article's title and its first four sentences into a single string. In this way, the target input is always the same over both genres, and it changes over targets only in the company names.", | |
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| "text": "Feature Extractor. As shared feature extractor, we adopt the pretrained BERT base uncased model (Devlin et al., 2019) .", | |
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| "end": 117, | |
| "text": "(Devlin et al., 2019)", | |
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| "text": "Stance Classifier. The stance label is predicted with a dense layer followed by a softmax operation operating on the hidden state h [CLS] of the special classification embedding [CLS]:", | |
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| { | |
| "text": "EQUATION", | |
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| "raw_str": "y s = softmax(W s h [CLS] + b s )", | |
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| "text": "The stance classifier is trained with categorical cross-entropy.", | |
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| "sec_num": "3.2" | |
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| "text": "Genre Discriminator. The genre discriminator aims to predict gender labels of samples (Twitter or news). The feature extractor parameters are optimized to maximize the loss of the genre discriminator, thus encouraging BERT to generate genreinvariant features. In practice, the hidden state", | |
| "cite_spans": [], | |
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| { | |
| "text": "h [CLS]", | |
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| "text": "is first fed to a Gradient Reversal Layer (GRL, Ganin and Lempitsky (2014) ). During the forward propagation, the GRL acts as an identity transformation:", | |
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| "text": "Ganin and Lempitsky (2014)", | |
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| "text": "EQUATION", | |
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| "raw_str": "GRL \u03bb (x) = x", | |
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| "section": "Models", | |
| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "but, during the backpropagation, it multiplies the gradient by a negative factor \u03bb:", | |
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| { | |
| "text": "\u03b4GRL \u03bb (x)) \u03b4x = \u2212\u03bbI (3)", | |
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| "sec_num": "3.2" | |
| }, | |
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| "text": "The genre label y g is finally obtained with a dense layer followed by a sigmoid operation:", | |
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| "section": "Models", | |
| "sec_num": "3.2" | |
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| { | |
| "text": "EQUATION", | |
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| "raw_str": "y g = sigmoid(W g GRL(h [CLS] ) + b g )", | |
| "eq_num": "(4)" | |
| } | |
| ], | |
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| "sec_num": "3.2" | |
| }, | |
| { | |
| "text": "The genre discriminator is trained with binary cross-entropy.", | |
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| "text": "Joint Learning. The two components are jointly trained, resulting in the total loss:", | |
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| { | |
| "text": "L total = L stance + L genre (5)", | |
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| "text": "The GI component is adversarial because it is trained to maximise the loss, while the SD component attempts to minimise it. In this way, the more the GI component is unable to correctly classify the samples, the more the system has learned to extract genre-invariant features.", | |
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| "text": "Baselines. We report results with the three baseline models proposed in Conforti et al. (2020a) : a dummy random and majority vote baseline, abd BertEmb, an MLP leveraging sentence-BERT embeddings (Reimers and Gurevych, 2019) ; moreover, we also consider two further baselines:", | |
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| "text": "(Reimers and Gurevych, 2019)", | |
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| "section": "Experimental Setting", | |
| "sec_num": "4" | |
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| "text": "\u2022 BERT news : A vanilla BERT finetuned on news samples only; \u2022 BERT CoT rain : A vanilla BERT finetuned on Tweet and news samples, but without the adversarial component (Blum and Mitchell, 1998; Chen et al., 2011) .", | |
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| "start": 169, | |
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| "text": "(Blum and Mitchell, 1998;", | |
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| "text": "Chen et al., 2011)", | |
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| "section": "Experimental Setting", | |
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| "text": "Training Setting and Preprocessing. We train in a cross-target setting (train on three mergers, test on the fourth) with the Adam optimizer. For each configuration, we randomly select 20% of the training samples as heldout data. For experiments with adversarial cross-genre SD, we randomly select a number of Twitter samples equal to the news training samples (i.e. we double the size of the training set). This ratio was found to perform best in preliminary experiments (refer to the Appendix for further details). The test set contains news samples only. We lowercase both tweets and news samples.", | |
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| "section": "Experimental Setting", | |
| "sec_num": "4" | |
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| { | |
| "text": "Hyperparameters. We set 128 as maximum sample length (including special tokens). We initialize our architecture with BERT large uncased 1 . BERT's weights are updated during training. We train using Adam (Kingma and Ba, 2015) on baatches of 23 samples, for a maximum of 70 epochs, with early stopping monitoring the SD loss on the development set.", | |
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| "section": "Experimental Setting", | |
| "sec_num": "4" | |
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| { | |
| "text": "Evaluation. As in Conforti et al. (2020a,b) , and in line with other works on news SD (Hanselowski et al., 2018 (Hanselowski et al., , 2019 , we report on macro-averaged F 1 and consider both per-target operation scores, and average scores weighted by target operation size. For the adversarial cross-genre experiments, we compute accuracy for the binary GI task. For computing the evaluation metrics, we use the sklearn's implementations 2 .", | |
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| "text": "Conforti et al. (2020a,b)", | |
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| "text": "(Hanselowski et al., 2018", | |
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| "text": "(Hanselowski et al., , 2019", | |
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| "section": "Experimental Setting", | |
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| }, | |
| { | |
| "text": "Computing Infrastructure. We run experiments on an NVIDIA GeForce GTX 1080 GPU. ", | |
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| "section": "Experimental Setting", | |
| "sec_num": "4" | |
| }, | |
| { | |
| "text": "In this Section, we report on our cross-genre SD experiments. The discussion is organized around three research questions.", | |
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| "section": "Experiments and Discussion", | |
| "sec_num": "5" | |
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| "text": "RQ1 What is the effect on news SD of including annotated data from a different genre?", | |
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| "text": "As shown in Table 3 , BERT CoT rain performs better than all baselines, including the BERT news model, which was trained on in-genre data only. This seems to suggest that exposing the model to in-domain stance-annotated data, even if from a completely different genre, improves the generalizability over unseen targets.", | |
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| "text": "RQ2 Is adversarial training effective to improve cross-genre SD?", | |
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| "text": "Adding an adversarial component to the BERT CoT rain model leads to gains in performance over all considered targets, with improvements ranging from +0.5 (AET HUM) and +4.0 (ANTM CI) in macro-averaged F 1 score. (Table 3) . Such performance gains are driven by improved performance over all stance labels (Table 4) , with refute samples benefiting most from the adversarial component. Such results suggest that a model which is punished for identifying the input's genre can still perform SD.", | |
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| "text": "RQ3 How does a decrease in GI through adversarial training correlate with SD performance? To understand to which extent genre-invariant representations are useful for SD, we experiment with different values of \u03bb, the GRL hyperparameter in Equation 3. As expected, GI performance lowers with increasing \u03bb (Table 4) , reaching 13.7 GI accuracy for the model with \u03bb = 1; this proves the GRL efficacy in forcing the model to learn genreindependent features. However, this also correlates with a steady decrease in SD performance, which holds true all target operations (Table 3) , with the only exception of the relatively small CI ESRX target, which also exhibit very strong label unbalancy (Table 1) .", | |
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| "text": "Moving to single-label classification, higher losses in performance are observed for comment and unrelated samples (resp. \u221213.5 and \u22128.3 in weighted accuracy), while support and refute label seem to be more robust to changes in the values of \u03bb. A possible explanation for this might be in the stylistic differences between the two corpora: unrelated and comment samples in the Twitter WT-WT corpus were often retrieved because of keywords homonymy 3 , and, as such, they tend to discuss completely different topics; on the contrary, such samples in the news STANDER corpus are actually covering the target companies. For this reason, completely genre-unaware knowledge transfer might not be optimal for those stance labels. This is in line with previous work by McHardy et al. (2019) on satire detection, and seems to indicate that, while learning partially genre-invariant features is beneficial for cross-target performance, features which are completely opaque with respect to the genre component are not ideal for SD.", | |
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| "text": "In this paper, we discussed the new task of crossgenre SD: our experiments with a range of BERTbased architectures show that partially obfuscating the genre component through adversarial training leads to better generalization, especially considering low-frequency labels. Cross-genre SD thus constitutes a promising future research direction. Future work might include experiments using different underlying feature extractors, such as RoBERTa, or with adapters, to study the robustness of cross-genre SD over modeling choices. The integration of cross-genre and cross-domain adaptation, possibly in a multi-task setting as in Conforti et al. (2020a) , also offers interesting ideas for future investigation.", | |
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| "text": "In this Appendix, we report on the results of preliminary experiments which were run in order to find the best proportion between news and Twitter samples. We consider six settings, with increasing proportion of Twitter to news samples: 50%, 100%, 150%, 200%, 250% and a last setting in which all Twitter samples were included. For each setting, experiments were run in a cross-validation setting (training on data on three operations, and testing on the fourth). Table 5 : Stance Detection performance with the genreadversarial model (\u03bb = 0.2), by adding different proportion of Twitter data from the WT-WT corpus to the STANDER news samples. A % of 100 corresponds to the proportion used in the experiments reported in the paper. all corresponds to all the samples in the WT-WT corpus (33,668).", | |
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| "text": "Interestingly, we observe gains in overall performance w.r.t. the BERT news baseline, which is trained on news data only (Table 5) , with all adversarial models. This holds true even in the case of the model trained on the union of STANDER (2945) with all tweets from the WT-WT corpus (30711): the model's considerable performance gain with respect to the BERT news model testifies the ability of the adversarial model to learn partially genre-invariant features even when exposed to extremely unbalanced training data. Singlelabel results (Table 6) show that increasing the ratio of Twitter samples included in the training data tends to correlate with performance gains in recall, at the expense of losses in performance. The best news-to-tweets ratio lies between 100 and 250, with small differences between target operations and stance labels. Thus, our adversarial crossgenre models seem to be relatively robust over the exact amount of out-of-genre samples which are included during training. Table 6 : Per-label detailed performance when adding different percentage of tweets to the STANDER news samples. A % of 100 corresponds to the proportion used in the experiments reported in the paper. All results are obtained with the genre-adversarial model (\u03bb = 0.2). Considering macro-averaged Precision, Recall and F 1 measures, weighted according to the target operation's size.", | |
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| "text": "https://tfhub.dev/tensorflow/bert en uncased L-12 H-768 A-12/3 2 https://scikit-learn.org/stable/modules/classes.html# module-sklearn.metrics", | |
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| { | |
| "text": "For example, 'cvs' not referring to the company 'CVS Health', but used as as plural of 'resume'.", | |
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| { | |
| "text": "We thank the anonymous reviewers of this paper for their efforts and for the constructive comments and suggestions. We gratefully acknowledge funding from the Keynes Fund, University of Cambridge (grant no. JHOQ). CC is grateful to NERC DREAM CDT (grant no. 1945246) for partially funding this work. CG and FT are thankful to the Cambridge Endowment for Research in Finance (CERF).", | |
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