Is Stance Detection Topic-Independent and Cross-topic Generalizable? - A Reproduction Study
Workshop on Argument Mining (ArgMining), 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 topicindependent 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
Computation Linguistics and Text Mining Lab, Vrije Universiteit Amsterdam
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Affiliation as printed
Leiden Institute of Advanced Computer Science , Leiden University
Leiden Institute of Advanced Computer Science, Leiden University
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Affiliation as printed
Computation Linguistics and Text Mining Lab , Vrije Universiteit Amsterdam
Computation Linguistics and Text Mining Lab, Vrije Universiteit Amsterdam
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Eindhoven University of Technology · Vrije Universiteit Amsterdam
Affiliation as printed
Computation Linguistics and Text Mining Lab , Vrije Universiteit Amsterdam
Dept. of Mathematics and Computer Science , Eindhoven University of Technology
Computation Linguistics and Text Mining Lab, Vrije Universiteit Amsterdam
Dept. of Mathematics and Computer Science, Eindhoven University of Technology
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