@inproceedings{rajani-etal-2020-esprit,
title = "{ESPRIT}: Explaining Solutions to Physical Reasoning Tasks",
author = "Rajani, Nazneen Fatema and
Zhang, Rui and
Tan, Yi Chern and
Zheng, Stephan and
Weiss, Jeremy and
Vyas, Aadit and
Gupta, Abhijit and
Xiong, Caiming and
Socher, Richard and
Radev, Dragomir",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.706",
doi = "10.18653/v1/2020.acl-main.706",
pages = "7906--7917",
abstract = "Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at \url{https://github.com/salesforce/esprit}.",
}
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<abstract>Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at https://github.com/salesforce/esprit.</abstract>
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%0 Conference Proceedings
%T ESPRIT: Explaining Solutions to Physical Reasoning Tasks
%A Rajani, Nazneen Fatema
%A Zhang, Rui
%A Tan, Yi Chern
%A Zheng, Stephan
%A Weiss, Jeremy
%A Vyas, Aadit
%A Gupta, Abhijit
%A Xiong, Caiming
%A Socher, Richard
%A Radev, Dragomir
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F rajani-etal-2020-esprit
%X Neural networks lack the ability to reason about qualitative physics and so cannot generalize to scenarios and tasks unseen during training. We propose ESPRIT, a framework for commonsense reasoning about qualitative physics in natural language that generates interpretable descriptions of physical events. We use a two-step approach of first identifying the pivotal physical events in an environment and then generating natural language descriptions of those events using a data-to-text approach. Our framework learns to generate explanations of how the physical simulation will causally evolve so that an agent or a human can easily reason about a solution using those interpretable descriptions. Human evaluations indicate that ESPRIT produces crucial fine-grained details and has high coverage of physical concepts compared to even human annotations. Dataset, code and documentation are available at https://github.com/salesforce/esprit.
%R 10.18653/v1/2020.acl-main.706
%U https://aclanthology.org/2020.acl-main.706
%U https://doi.org/10.18653/v1/2020.acl-main.706
%P 7906-7917
Markdown (Informal)
[ESPRIT: Explaining Solutions to Physical Reasoning Tasks](https://aclanthology.org/2020.acl-main.706) (Rajani et al., ACL 2020)
ACL
- Nazneen Fatema Rajani, Rui Zhang, Yi Chern Tan, Stephan Zheng, Jeremy Weiss, Aadit Vyas, Abhijit Gupta, Caiming Xiong, Richard Socher, and Dragomir Radev. 2020. ESPRIT: Explaining Solutions to Physical Reasoning Tasks. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 7906–7917, Online. Association for Computational Linguistics.