Policy-focused Stance Detection in Parliamentary Debate Speeches

Gavin Abercrombie, Riza Theresa Batista-Navarro

Research output: Contribution to journalArticlepeer-review

Abstract

Legislative debate transcripts provide citizens with information about the activities of their elected representatives, but are difficult for people to process. We propose the novel task of policy-focused stance detection, in which both the policy proposals under debate and the position of the speakers towards those proposals are identified. We adapt a previously existing dataset to include manual annotations of policy preferences, an established schema from political science. We evaluate a range of approaches to the automatic classification of policy preferences and speech sentiment polarity, including transformer-based text representations and a multi-task learning paradigm. We find that it is possible to identify the policies under discussion using features derived from the speeches, and that incorporating motion-dependent debate modelling, previously used to classify speech sentiment, also improves performance in the classification of policy preferences. We analyse the output of the best performing system, finding that discriminating features for the task are highly domain-specific, and that speeches that address policy preferences proposed by members of the same party can be among the most difficult to predict.
Original languageEnglish
Pages (from-to)81-96
Number of pages16
JournalThe Northern European Journal of Language Technology
Volume8
Issue number1
Early online date1 Jul 2022
DOIs
Publication statusPublished - 31 Dec 2022

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