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

• URL: http://arxiv.org/abs/2110.07693v1
• Date: Thu, 14 Oct 2021 20:03:36 GMT
• Title: Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study
• Authors: Myrthe Reuver and Suzan Verberne and Roser Morante and Antske Fokkens
• Abstract summary: Cross-topic stance detection is the task to automatically detect stances (pro, against, or neutral) on unseen topics. We reproduce state-of-the-art cross-topic stance detection work, and analyze its results. We ask: What extent is stance detection topic-independent and generalizable across topics? We conclude that investigating performance on different topics, and addressing topic-specific vocabulary and context, is a future avenue for cross-topic stance detection.
• Score: 6.047731445033151
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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