Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models

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

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.
Anthology ID:
D18-1370
Volume:
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing
Month:
October-November
Year:
2018
Address:
Brussels, Belgium
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
3326–3338
Language:
URL:
https://aclanthology.org/D18-1370
DOI:
10.18653/v1/D18-1370
Bibkey:
Cite (ACL):
Anne Lauscher, Goran Glavaš, Simone Paolo Ponzetto, and Kai Eckert. 2018. Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 3326–3338, Brussels, Belgium. Association for Computational Linguistics.
Cite (Informal):
Investigating the Role of Argumentation in the Rhetorical Analysis of Scientific Publications with Neural Multi-Task Learning Models (Lauscher et al., EMNLP 2018)
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