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Cross-lingual transfer-learning approach to negation scope resolution


Shaitarova, Anastassia; Furrer, Lenz; Rinaldi, Fabio (2020). Cross-lingual transfer-learning approach to negation scope resolution. In: Swiss Text Analytics Conference & Conference on Natural Language Processing 2020, Zurich, 23 June 2020 - 25 June 2020.

Abstract

Detecting instances of negation in text is crucially important for several applications, yet it is often neglected. Several decades of research in automated negation detection have not yet provided a reliable solution, especially in a multilingual context. Negation scope resolution poses particular challenges since identifying the scope of influence of a negation cue in a sentence requires a deeper level of natural language understanding. Little work has been done on negation scope resolution in languages other than English. Meanwhile, transfer learning is in wide use and large multilingual models are available to the public. This paper explores the feasibility of a cross-lingual transfer-learning approach to negation scope resolution. Preliminary experiments with the Multilingual BERT model and data in English, French, and Spanish show solid results with the highest F1-score 84.73 on zero-shot transfer between English and French.

Abstract

Detecting instances of negation in text is crucially important for several applications, yet it is often neglected. Several decades of research in automated negation detection have not yet provided a reliable solution, especially in a multilingual context. Negation scope resolution poses particular challenges since identifying the scope of influence of a negation cue in a sentence requires a deeper level of natural language understanding. Little work has been done on negation scope resolution in languages other than English. Meanwhile, transfer learning is in wide use and large multilingual models are available to the public. This paper explores the feasibility of a cross-lingual transfer-learning approach to negation scope resolution. Preliminary experiments with the Multilingual BERT model and data in English, French, and Spanish show solid results with the highest F1-score 84.73 on zero-shot transfer between English and French.

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Additional indexing

Item Type:Conference or Workshop Item (Paper), refereed, original work
Communities & Collections:06 Faculty of Arts > Institute of Computational Linguistics
Dewey Decimal Classification:000 Computer science, knowledge & systems
410 Linguistics
Scopus Subject Areas:Physical Sciences > General Computer Science
Language:English
Event End Date:25 June 2020
Deposited On:22 Jan 2021 14:44
Last Modified:23 Jan 2021 21:01
Publisher:CEUR-WS
Series Name:CEUR Workshop Proceedings
ISSN:1613-0073
OA Status:Green
Free access at:Publisher DOI. An embargo period may apply.
Official URL:http://ceur-ws.org/Vol-2624/paper13.pdf

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