Investigating the role of argumentation in the rhetorical analysis of scientific publications with neural multi-task learning models

Lauscher, Anne ; Glavaš, Goran ; Ponzetto, Simone Paolo ; Eckert, Kai

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URN: urn:nbn:de:bsz:180-madoc-460867
Document Type: Conference or workshop publication
Year of publication: 2018
Book title: EMNLP 2018 : Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, Brussels, Belgium, Oct. 31-Nov. 4
Page range: 3326-3338
Conference title: 2018 Conference on Empirical Methods in Natural Language Processing
Location of the conference venue: Brussels, Belgium
Date of the conference: October 31-November 4, 2018
Publisher: Riloff, Ellen
Place of publication: Stroudsburg, PA
Publishing house: Association for Computational Linguistics
ISBN: 978-1-948087-84-1
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Publication language: English
Institution: School of Business Informatics and Mathematics > Text Analytics for Interdisciplinary Research (Juniorprofessur) (Glavaš 2017-2021)
School of Business Informatics and Mathematics > Information Systems III: Enterprise Data Analysis (Ponzetto 2016-)
Subject: 004 Computer science, internet
Keywords (English): argument mining , scientific publication mining , scitorics , multi-task learning , natural language processing , deep learning
Abstract: Exponential growth in the number of scientific publications yields the need for effective automatic analysis of rhetorical aspects of scientific writing. Acknowledging the argumentative nature of scientific text, in this work we investigate the link between the argumentative structure of scientific publications and rhetorical aspects such as discourse categories or citation contexts. To this end, we (1) augment a corpus of scientific publications annotated with four layers of rhetoric annotations with argumentation annotations and (2) investigate neural multi-task learning architectures combining argument extraction with a set of rhetorical classification tasks. By coupling rhetorical classifiers with the extraction of argumentative components in a joint multi-task learning setting, we obtain significant performance gains for different rhetorical analysis tasks.
Additional information: Online-Ressource

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