Exploration of contrastive learning strategies toward more robust stance detection systems

Abstract

Stance Detection, in general, is the task of identifying the author’s position on controversial topics. In Natural Language Processing, Stance Detection extracts the author’s attitude from the text written toward an issue to determine whether the author supports the issue or is against the issue. The studies analyzing public opinion on social media, especially in relation to political and social concerns, heavily rely on Stance Detection. The linguistics of social media texts and articles are often unstructured. Hence, the Stance Detection systems needed to be robust when identifying the position or stance of an author on a topic. This thesis seeks to contribute to the ongoing research on Stance Detection. This research proposes a Contrastive Learning approach to achieve the goal of learning sentence representations leading to more robust Stance Detection systems. Further, this thesis explores the possibility of extending the proposed methodology to detect stances from unlabeled or unannotated data. The stance of an author towards a topic can be implicit (through reasoning) or explicit; The proposed method learns the sentence representations in a contrastive fashion to learn the sentence-level meaning. The Contrastive Learning of sentence representations results in bringing similar examples in the Sentence Representation space belonging to the same stance close to each other, whereas the dissimilar examples are far apart. The proposed method also accommodates the token-level meaning by combining the Masked Language Modeling objective (similar to BERT pretraining) with the Contrastive Learning objective. [...]