Abstract
We show that explicit pragmatic inference aids in correctly generating and following natural language instructions for complex, sequential tasks. Our pragmatics-enabled models reason about why speakers produce certain instructions, and about how listeners will react upon hearing them. Like previous pragmatic models, we use learned base listener and speaker models to build a pragmatic speaker that uses the base listener to simulate the interpretation of candidate descriptions, and a pragmatic listener that reasons counterfactually about alternative descriptions. We extend these models to tasks with sequential structure. Evaluation of language generation and interpretation shows that pragmatic inference improves state-of-the-art listener models (at correctly interpreting human instructions) and speaker models (at producing instructions correctly interpreted by humans) in diverse settings.- Anthology ID:
- N18-1177
- Volume:
- Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
- Month:
- June
- Year:
- 2018
- Address:
- New Orleans, Louisiana
- Editors:
- Marilyn Walker, Heng Ji, Amanda Stent
- Venue:
- NAACL
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 1951–1963
- Language:
- URL:
- https://aclanthology.org/N18-1177
- DOI:
- 10.18653/v1/N18-1177
- Cite (ACL):
- Daniel Fried, Jacob Andreas, and Dan Klein. 2018. Unified Pragmatic Models for Generating and Following Instructions. In Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers), pages 1951–1963, New Orleans, Louisiana. Association for Computational Linguistics.
- Cite (Informal):
- Unified Pragmatic Models for Generating and Following Instructions (Fried et al., NAACL 2018)
- PDF:
- https://preview.aclanthology.org/landing_page/N18-1177.pdf
- Code
- dpfried/pragmatic-instructions