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
- Editors:
- Martha Palmer, Rebecca Hwa, Sebastian Riedel
- 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
- 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)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-4/D17-1128.pdf
- Data
- FrameNet