Policy Preference Detection in Parliamentary Debate Motions

Gavin Abercrombie, Federico Nanni, Riza Theresa Batista-Navarro, Simone Paolo Ponzetto

Research output: Chapter in Book/Report/Conference proceedingConference contribution

10 Citations (Scopus)


Debate motions (proposals) tabled in the UK Parliament contain information about the stated policy preferences of the Members of Parliament who propose them, and are key to the analysis of all subsequent speeches given in response to them. We attempt to automatically label debate motions with codes from a pre-existing coding scheme developed by political scientists for the annotation and analysis of political parties’ manifestos. We develop annotation guidelines for the task of applying these codes to debate motions at two levels of granularity and produce a dataset of manually labelled examples. We evaluate the annotation process and the reliability and utility of the labelling scheme, finding that inter-annotator agreement is comparable with that of other studies conducted on manifesto data. Moreover, we test a variety of ways of automatically labelling motions with the codes, ranging from similarity matching to neural classification methods, and evaluate them against the gold standard labels. From these experiments, we note that established supervised baselines are not always able to improve over simple lexical heuristics. At the same time, we detect a clear and evident benefit when employing BERT, a state-of-the-art deep language representation model, even in classification scenarios with over 30 different labels and limited amounts of training data.
Original languageEnglish
Title of host publicationProceedings of the 23rd Conference on Computational Natural Language Learning (CoNLL)
Place of PublicationUnited States
PublisherAssociation for Computational Linguistics
Number of pages11
Publication statusPublished - 2019
Event 23rd Conference on Computational Natural Language Learning 2019 - , Hong Kong
Duration: 3 Nov 20194 Nov 2019


Conference 23rd Conference on Computational Natural Language Learning 2019
Abbreviated titleCoNLL 2019
Country/TerritoryHong Kong


  • Sentiment Analysis
  • topic detection
  • Political science
  • Natural Language Processing
  • Hansard


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