A Joint Sequential and Relational Model for Frame-Semantic Parsing

Bishan Yang, Tom Mitchell


Abstract
We introduce a new method for frame-semantic parsing that significantly improves the prior state of the art. Our model leverages the advantages of a deep bidirectional LSTM network which predicts semantic role labels word by word and a relational network which predicts semantic roles for individual text expressions in relation to a predicate. The two networks are integrated into a single model via knowledge distillation, and a unified graphical model is employed to jointly decode frames and semantic roles during inference. Experiments on the standard FrameNet data show that our model significantly outperforms existing neural and non-neural approaches, achieving a 5.7 F1 gain over the current state of the art, for full frame structure extraction.
Anthology ID:
D17-1128
Volume:
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
Month:
September
Year:
2017
Address:
Copenhagen, Denmark
Venue:
EMNLP
SIG:
SIGDAT
Publisher:
Association for Computational Linguistics
Note:
Pages:
1247–1256
Language:
URL:
https://aclanthology.org/D17-1128
DOI:
10.18653/v1/D17-1128
Bibkey:
Cite (ACL):
Bishan Yang and Tom Mitchell. 2017. A Joint Sequential and Relational Model for Frame-Semantic Parsing. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1247–1256, Copenhagen, Denmark. Association for Computational Linguistics.
Cite (Informal):
A Joint Sequential and Relational Model for Frame-Semantic Parsing (Yang & Mitchell, EMNLP 2017)
Copy Citation:
PDF:
https://aclanthology.org/D17-1128.pdf
Data
FrameNet