Semantic Parsing of Brief and Multi-Intent Natural Language Utterances
Logan Lebanoff, Charles Newton, Victor Hung, Beth Atkinson, John Killilea, Fei Liu
Abstract
Many military communication domains involve rapidly conveying situation awareness with few words. Converting natural language utterances to logical forms in these domains is challenging, as these utterances are brief and contain multiple intents. In this paper, we present a first effort toward building a weakly-supervised semantic parser to transform brief, multi-intent natural utterances into logical forms. Our findings suggest a new “projection and reduction” method that iteratively performs projection from natural to canonical utterances followed by reduction of natural utterances is the most effective. We conduct extensive experiments on two military and a general-domain dataset and provide a new baseline for future research toward accurate parsing of multi-intent utterances.- Anthology ID:
- 2021.adaptnlp-1.25
- Volume:
- Proceedings of the Second Workshop on Domain Adaptation for NLP
- Month:
- April
- Year:
- 2021
- Address:
- Kyiv, Ukraine
- Editors:
- Eyal Ben-David, Shay Cohen, Ryan McDonald, Barbara Plank, Roi Reichart, Guy Rotman, Yftah Ziser
- Venue:
- AdaptNLP
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 255–262
- Language:
- URL:
- https://aclanthology.org/2021.adaptnlp-1.25
- DOI:
- Cite (ACL):
- Logan Lebanoff, Charles Newton, Victor Hung, Beth Atkinson, John Killilea, and Fei Liu. 2021. Semantic Parsing of Brief and Multi-Intent Natural Language Utterances. In Proceedings of the Second Workshop on Domain Adaptation for NLP, pages 255–262, Kyiv, Ukraine. Association for Computational Linguistics.
- Cite (Informal):
- Semantic Parsing of Brief and Multi-Intent Natural Language Utterances (Lebanoff et al., AdaptNLP 2021)
- PDF:
- https://preview.aclanthology.org/emnlp-22-attachments/2021.adaptnlp-1.25.pdf