Compositional Task-Oriented Parsing as Abstractive Question Answering
Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, Claire Cardie
Abstract
Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. A popular approach to TOP is to apply seq2seq models to generate linearized parse trees. A more recent line of work argues that pretrained seq2seq2 models are better at generating outputs that are themselves natural language, so they replace linearized parse trees with canonical natural-language paraphrases that can then be easily translated into parse trees, resulting in so-called naturalized parsers. In this work we continue to explore naturalized semantic parsing by presenting a general reduction of TOP to abstractive question answering that overcomes some limitations of canonical paraphrasing. Experimental results show that our QA-based technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings.- Anthology ID:
- 2022.naacl-main.328
- Volume:
- Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Month:
- July
- Year:
- 2022
- Address:
- Seattle, United States
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 4418–4427
- Language:
- URL:
- https://aclanthology.org/2022.naacl-main.328
- DOI:
- 10.18653/v1/2022.naacl-main.328
- Cite (ACL):
- Wenting Zhao, Konstantine Arkoudas, Weiqi Sun, and Claire Cardie. 2022. Compositional Task-Oriented Parsing as Abstractive Question Answering. In Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 4418–4427, Seattle, United States. Association for Computational Linguistics.
- Cite (Informal):
- Compositional Task-Oriented Parsing as Abstractive Question Answering (Zhao et al., NAACL 2022)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/2022.naacl-main.328.pdf
- Code
- amazon-research/semantic-parsing-as-abstractive-qa
- Data
- TOPv2