GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion

Proceedings of the 28th International Conference on Computational Linguistics, pages 4847–4859 Barcelona, Spain (Online), December 8-13, 2020 4847 GPolS: A Contextual Graph-Based Language Model for Analyzing Parliamentary Debates and Political Cohesion Ramit Sawhney Netaji Subhas Institute of Technology ramits.co@nsit.net.in Arnav Wadhwa MIDAS, IIIT Delhi arnavw96@gmail.com Shivam Agarwal Manipal Institute of Technology shivamag99@gmail.com Rajiv Ratn Shah MIDAS, IIIT Delhi rajivratn@iiitd.ac.in Abstract Parliamentary debates present a valuable language resource for analyzing comprehensive op- tions in electing representatives under a functional, free society. However, the esoteric nature of political speech coupled with non-linguistic aspects such as political cohesion between party members presents a complex and underexplored task of contextual parliamentary debate analy- sis. We introduce GPolS, a neural model for political speech stance analysis jointly exploiting both semantic language representations and relations between debate transcripts, motions, and political party members. Through experiments on real-world English data, we provide a use case of GPolS as a tool for political speech analysis and polarity prediction. 1 Introduction Politics is broadly defined as the set of activities associated with the governance of a country or a re- gion. It involves various aspects that influence critical decisions having national importance. One such aspect is the conduct of parliamentary debates between political parties having ruling and opposition power. These debates discuss matters affecting the future development of a nation, such as economic and societal growth, policy reforms, and budget revisions. Records of such debates act as a valuable lan- guage resource as they provide a wealth of information regarding viewpoints of political representatives over critical societal factors (Abercrombie and Batista-Navarro, 2020b), and also for assessing political candidates and basing voting decisions (Utych, 2019). Analyzing sentiment in such political discourse is propelled by the developments in fields like behav- ioral economics that bring the psychological aspects of parliamentary decision-making to the forefront (Rheault, 2016). However, under Parliament’s Rules of Behavior,1 the language used in political debates is complex, laden with domain-specific procedural jargon used in the political realm, and obscure (Aber- crombie and Batista-Navarro, 2018a). This esoteric and tedious nature of political debates makes their analysis complex, forming a barrier to ordinary citizen’s insights into political stances and wide-ranging consequences they entail (Edelman, 1985). The good news is that natural language processing (NLP) shows promise for analyzing voluminous political debates and breaking the understanding barrier towards political ideology to help make informed voting decisions (Davoodi et al., 2020; Eidelman et al., 2018). However, conventional language models (Hasan and Ng, 2013) may not generalize well on understanding the obscure linguistic styles of political debates. This complexity arises due to the lack of political context and procedural parliamentary jargon in generic corpora over which traditional text representation models are trained (Pennington et al., 2014; Bojanowski et al., 2017). For instance, in the political context, Red State is a state that primarily votes for Republicans, whereas a Blue State votes primarily for Democrats. Leveraging the success of unsu- pervised pre-training in NLP (Devlin et al., 2019; Liu et al., 2019), fine-tuning pre-trained …

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