Sources of Transfer in Multilingual Named Entity Recognition

David Mueller, Nicholas Andrews, Mark Dredze


Abstract
Named-entities are inherently multilingual, and annotations in any given language may be limited. This motivates us to consider polyglot named-entity recognition (NER), where one model is trained using annotated data drawn from more than one language. However, a straightforward implementation of this simple idea does not always work in practice: naive training of NER models using annotated data drawn from multiple languages consistently underperforms models trained on monolingual data alone, despite having access to more training data. The starting point of this paper is a simple solution to this problem, in which polyglot models are fine-tuned on monolingual data to consistently and significantly outperform their monolingual counterparts. To explain this phenomena, we explore the sources of multilingual transfer in polyglot NER models and examine the weight structure of polyglot models compared to their monolingual counterparts. We find that polyglot models efficiently share many parameters across languages and that fine-tuning may utilize a large number of those parameters.
Anthology ID:
2020.acl-main.720
Volume:
Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
Month:
July
Year:
2020
Address:
Online
Venue:
ACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
8093–8104
Language:
URL:
https://aclanthology.org/2020.acl-main.720
DOI:
10.18653/v1/2020.acl-main.720
Bibkey:
Cite (ACL):
David Mueller, Nicholas Andrews, and Mark Dredze. 2020. Sources of Transfer in Multilingual Named Entity Recognition. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 8093–8104, Online. Association for Computational Linguistics.
Cite (Informal):
Sources of Transfer in Multilingual Named Entity Recognition (Mueller et al., ACL 2020)
Copy Citation:
PDF:
https://preview.aclanthology.org/nodalida-main-page/2020.acl-main.720.pdf
Software:
 2020.acl-main.720.Software.zip
Dataset:
 2020.acl-main.720.Dataset.pdf
Video:
 http://slideslive.com/38929424
Code
 davidandym/multilingual-NER