Semantic Role Labeling with Iterative Structure Refinement

Chunchuan Lyu, Shay B. Cohen, Ivan Titov


Abstract
Modern state-of-the-art Semantic Role Labeling (SRL) methods rely on expressive sentence encoders (e.g., multi-layer LSTMs) but tend to model only local (if any) interactions between individual argument labeling decisions. This contrasts with earlier work and also with the intuition that the labels of individual arguments are strongly interdependent. We model interactions between argument labeling decisions through iterative refinement. Starting with an output produced by a factorized model, we iteratively refine it using a refinement network. Instead of modeling arbitrary interactions among roles and words, we encode prior knowledge about the SRL problem by designing a restricted network architecture capturing non-local interactions. This modeling choice prevents overfitting and results in an effective model, outperforming strong factorized baseline models on all 7 CoNLL-2009 languages, and achieving state-of-the-art results on 5 of them, including English.
Anthology ID:
D19-1099
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:
1071–1082
Language:
URL:
https://aclanthology.org/D19-1099
DOI:
10.18653/v1/D19-1099
Bibkey:
Copy Citation:
PDF:
https://aclanthology.org/D19-1099.pdf
Attachment:
 D19-1099.Attachment.pdf
Code
 ChunchuanLv/Iterative_Inference