Semi-Supervised Semantic Role Labeling with Cross-View Training

Rui Cai, Mirella Lapata


Abstract
The successful application of neural networks to a variety of NLP tasks has provided strong impetus to develop end-to-end models for semantic role labeling which forego the need for extensive feature engineering. Recent approaches rely on high-quality annotations which are costly to obtain, and mostly unavailable in low resource scenarios (e.g., rare languages or domains). Our work aims to reduce the annotation effort involved via semi-supervised learning. We propose an end-to-end SRL model and demonstrate it can effectively leverage unlabeled data under the cross-view training modeling paradigm. Our LSTM-based semantic role labeler is jointly trained with a sentence learner, which performs POS tagging, dependency parsing, and predicate identification which we argue are critical to learning directly from unlabeled data without recourse to external pre-processing tools. Experimental results on the CoNLL-2009 benchmark dataset show that our model outperforms the state of the art in English, and consistently improves performance in other languages, including Chinese, German, and Spanish.
Anthology ID:
D19-1094
Volume:
Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP)
Month:
November
Year:
2019
Address:
Hong Kong, China
Venues:
EMNLP | IJCNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1018–1027
Language:
URL:
https://aclanthology.org/D19-1094
DOI:
10.18653/v1/D19-1094
Bibkey:
Cite (ACL):
Rui Cai and Mirella Lapata. 2019. Semi-Supervised Semantic Role Labeling with Cross-View Training. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP), pages 1018–1027, Hong Kong, China. Association for Computational Linguistics.
Cite (Informal):
Semi-Supervised Semantic Role Labeling with Cross-View Training (Cai & Lapata, EMNLP 2019)
Copy Citation:
PDF:
https://aclanthology.org/D19-1094.pdf