Is Stance Detection Topic-Independent and Cross-topic Generalizable? -- A Reproduction Study
arXiv (Cornell University), pp. 46–56
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.
Authors 4
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Affiliation as printed
Computation Linguistics and Text Mining Lab , Vrije Universiteit Amsterdam
Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Leiden University · Vrije Universiteit Amsterdam
Affiliation as printed
Leiden Institute of Advanced Computer Science , Leiden University
Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Affiliation as printed
Computation Linguistics and Text Mining Lab , Vrije Universiteit Amsterdam
Vrije Universiteit Amsterdam, Amsterdam, Netherlands
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Antske Fokkens Aachen
Leiden University · Vrije Universiteit Amsterdam · Eindhoven University of Technology
Affiliation as printed
Computation Linguistics and Text Mining Lab , Vrije Universiteit Amsterdam
Dept. of Mathematics and Computer Science , Eindhoven University of Technology
Leiden University, Leiden, Netherlands
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