Graph-Based Decoding for Task Oriented Semantic Parsing
Jeremy Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, Peter Shaw
Abstract
The dominant paradigm for semantic parsing in recent years is to formulate parsing as a sequence-to-sequence task, generating predictions with auto-regressive sequence decoders. In this work, we explore an alternative paradigm. We formulate semantic parsing as a dependency parsing task, applying graph-based decoding techniques developed for syntactic parsing. We compare various decoding techniques given the same pre-trained Transformer encoder on the TOP dataset, including settings where training data is limited or contains only partially-annotated examples. We find that our graph-based approach is competitive with sequence decoders on the standard setting, and offers significant improvements in data efficiency and settings where partially-annotated data is available.- Anthology ID:
- 2021.findings-emnlp.341
- Volume:
- Findings of the Association for Computational Linguistics: EMNLP 2021
- Month:
- November
- Year:
- 2021
- Address:
- Punta Cana, Dominican Republic
- Editors:
- Marie-Francine Moens, Xuanjing Huang, Lucia Specia, Scott Wen-tau Yih
- Venue:
- Findings
- SIG:
- SIGDAT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4057–4065
- Language:
- URL:
- https://aclanthology.org/2021.findings-emnlp.341
- DOI:
- 10.18653/v1/2021.findings-emnlp.341
- Cite (ACL):
- Jeremy Cole, Nanjiang Jiang, Panupong Pasupat, Luheng He, and Peter Shaw. 2021. Graph-Based Decoding for Task Oriented Semantic Parsing. In Findings of the Association for Computational Linguistics: EMNLP 2021, pages 4057–4065, Punta Cana, Dominican Republic. Association for Computational Linguistics.
- Cite (Informal):
- Graph-Based Decoding for Task Oriented Semantic Parsing (Cole et al., Findings 2021)
- PDF:
- https://preview.aclanthology.org/landing_page/2021.findings-emnlp.341.pdf