@inproceedings{fried-etal-2018-unified,
title = "Unified Pragmatic Models for Generating and Following Instructions",
author = "Fried, Daniel and
Andreas, Jacob and
Klein, Dan",
editor = "Walker, Marilyn and
Ji, Heng and
Stent, Amanda",
booktitle = "Proceedings of the 2018 Conference of the North {A}merican Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)",
month = jun,
year = "2018",
address = "New Orleans, Louisiana",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/N18-1177",
doi = "10.18653/v1/N18-1177",
pages = "1951--1963",
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.",
}
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%0 Conference Proceedings
%T Unified Pragmatic Models for Generating and Following Instructions
%A Fried, Daniel
%A Andreas, Jacob
%A Klein, Dan
%Y Walker, Marilyn
%Y Ji, Heng
%Y Stent, Amanda
%S Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long Papers)
%D 2018
%8 June
%I Association for Computational Linguistics
%C New Orleans, Louisiana
%F fried-etal-2018-unified
%X 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.
%R 10.18653/v1/N18-1177
%U https://aclanthology.org/N18-1177
%U https://doi.org/10.18653/v1/N18-1177
%P 1951-1963
Markdown (Informal)
[Unified Pragmatic Models for Generating and Following Instructions](https://aclanthology.org/N18-1177) (Fried et al., NAACL 2018)
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.