Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study

Research output: Chapter in Book / Report / Conference proceedingConference contributionAcademicpeer-review

Abstract

Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We successfully reproduce state-of-the-art cross-topic stance detection work (Reimers et. al, 2019), and systematically analyze its reproducibility. Our attention then turns to the cross-topic aspect of this work, and the specificity of topics in terms of vocabulary and socio-cultural context. We ask: To what extent is stance detection topic-independent and generalizable across topics? We compare the model{'}s performance on various unseen topics, and find topic (e.g. abortion, cloning), class (e.g. pro, con), and their interaction affecting the model{'}s performance. We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection.

References

Nils Reimers, Benjamin Schiller, Tilman Beck, Johannes Daxenberger, Christian Stab, and Iryna Gurevych. 2019. Classification and Clustering of Arguments with Contextualized Word Embeddings. In Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics, pages 567{--}578, Florence, Italy. Association for Computational Linguistics.
Original languageEnglish
Title of host publicationProceedings of the 8th Workshop on Argument Mining, co-located with the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP 2021)
Place of PublicationPunta Cana, the Dominican Republic
PublisherAssociation for Computational Linguistics, ACL Anthology
Pages46–56
Number of pages10
Publication statusPublished - Nov 2021

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