Come hither or go away? Recognising pre-electoral coalition signals in the news

Rehbein, Ines ; Ponzetto, Simone Paolo ; Adendorf, Anna ; Bahnsen, Oke ; Stoetzer, Lukas F. ; Stuckenschmidt, Heiner

URL:	https://aclanthology.org/2021.emnlp-main.615
Dokumenttyp:	Konferenzveröffentlichung
Erscheinungsjahr:	2021
Buchtitel:	Proceedings of the 2021 conference on empirical methods in natural language processing (EMNLP 2021)
Seitenbereich:	7798-7810
Veranstaltungstitel:	EMNLP 2021
Veranstaltungsort:	Punta Cana, Dominican Republic, online
Veranstaltungsdatum:	7.-11.11.2021
Herausgeber:	Moens, Marie-Francine ; Huang, Xuanjing ; Specia, Lucia ; Yih, Scott Wen-tau
Ort der Veröffentlichung:	Punta Cana, Dominican Republic
Verlag:	Association for Computational Linguistics
Sprache der Veröffentlichung:	Englisch
Einrichtung:	Außerfakultäre Einrichtungen > SFB 884 Außerfakultäre Einrichtungen > GESS - CDSS (SOWI)
Fachgebiet:	004 Informatik
Freie Schlagwörter (Englisch):	political text analysis
Abstract:	In this paper, we introduce the task of political coalition signal prediction from text, that is, the task of recognizing from the news coverage leading up to an election the(un)willingness of political parties to form a government coalition. We decompose our problem into two related, but distinct tasks: (i) predicting whether a reported statement from a politician or a journalist refers to a potential coalition and (ii) predicting the polarity of the signal — namely, whether the speaker is in favour of or against the coalition. For this, we explore the benefits of multi-task learning and investigate which setup and task formulation is best suited for each subtask. We evaluate our approach, based on hand-coded newspaper articles, covering elections in three countries (Ireland, Germany, Austria) and two languages (English, German). Our results show that the multi-task learning approach can further improve results over a strong monolingual transfer learning baseline.