A Multi-task Learning Approach to Adapting Bilingual Word Embeddings for Cross-lingual Named Entity Recognition

Dingquan Wang, Nanyun Peng, Kevin Duh


Abstract
We show how to adapt bilingual word embeddings (BWE’s) to bootstrap a cross-lingual name-entity recognition (NER) system in a language with no labeled data. We assume a setting where we are given a comparable corpus with NER labels for the source language only; our goal is to build a NER model for the target language. The proposed multi-task model jointly trains bilingual word embeddings while optimizing a NER objective. This creates word embeddings that are both shared between languages and fine-tuned for the NER task.
Anthology ID:
I17-2065
Volume:
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)
Month:
November
Year:
2017
Address:
Taipei, Taiwan
Venue:
IJCNLP
SIG:
Publisher:
Asian Federation of Natural Language Processing
Note:
Pages:
383–388
Language:
URL:
https://www.aclweb.org/anthology/I17-2065
DOI:
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PDF:
http://aclanthology.lst.uni-saarland.de/I17-2065.pdf