Leveraging Multilingual Training for Limited Resource Event Extraction

Andrew Hsi, Yiming Yang, Jaime Carbonell, Ruochen Xu


Abstract
Event extraction has become one of the most important topics in information extraction, but to date, there is very limited work on leveraging cross-lingual training to boost performance. We propose a new event extraction approach that trains on multiple languages using a combination of both language-dependent and language-independent features, with particular focus on the case where target domain training data is of very limited size. We show empirically that multilingual training can boost performance for the tasks of event trigger extraction and event argument extraction on the Chinese ACE 2005 dataset.
Anthology ID:
C16-1114
Volume:
Proceedings of COLING 2016, the 26th International Conference on Computational Linguistics: Technical Papers
Month:
December
Year:
2016
Address:
Osaka, Japan
Venue:
COLING
SIG:
Publisher:
The COLING 2016 Organizing Committee
Note:
Pages:
1201–1210
URL:
https://www.aclweb.org/anthology/C16-1114
DOI:
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PDF:
https://www.aclweb.org/anthology/C16-1114.pdf