@inproceedings{rajagopal-etal-2022-curie,
title = "{CURIE}: An Iterative Querying Approach for Reasoning About Situations",
author = "Rajagopal, Dheeraj and
Madaan, Aman and
Tandon, Niket and
Yang, Yiming and
Prabhumoye, Shrimai and
Ravichander, Abhilasha and
Clark, Peter and
Hovy, Eduard H",
editor = "Bosselut, Antoine and
Li, Xiang and
Lin, Bill Yuchen and
Shwartz, Vered and
Majumder, Bodhisattwa Prasad and
Lal, Yash Kumar and
Rudinger, Rachel and
Ren, Xiang and
Tandon, Niket and
Zouhar, Vil{\'e}m",
booktitle = "Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)",
month = may,
year = "2022",
address = "Dublin, Ireland",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.csrr-1.7",
doi = "10.18653/v1/2022.csrr-1.7",
pages = "49--63",
abstract = "Predicting the effects of unexpected situations is an important reasoning task, e.g., would cloudy skies help or hinder plant growth? Given a context, the goal of such situational reasoning is to elicit the consequences of a new situation (st) that arises in that context. We propose CURIE, a method to iteratively build a graph of relevant consequences explicitly in a structured situational graph (st graph) using natural language queries over a finetuned language model. Across multiple domains, CURIE generates st graphs that humans find relevant and meaningful in eliciting the consequences of a new situation (75{\%} of the graphs were judged correct by humans). We present a case study of a situation reasoning end task (WIQA-QA), where simply augmenting their input with st graphs improves accuracy by 3 points. We show that these improvements mainly come from a hard subset of the data, that requires background knowledge and multi-hop reasoning.",
}
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<abstract>Predicting the effects of unexpected situations is an important reasoning task, e.g., would cloudy skies help or hinder plant growth? Given a context, the goal of such situational reasoning is to elicit the consequences of a new situation (st) that arises in that context. We propose CURIE, a method to iteratively build a graph of relevant consequences explicitly in a structured situational graph (st graph) using natural language queries over a finetuned language model. Across multiple domains, CURIE generates st graphs that humans find relevant and meaningful in eliciting the consequences of a new situation (75% of the graphs were judged correct by humans). We present a case study of a situation reasoning end task (WIQA-QA), where simply augmenting their input with st graphs improves accuracy by 3 points. We show that these improvements mainly come from a hard subset of the data, that requires background knowledge and multi-hop reasoning.</abstract>
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%0 Conference Proceedings
%T CURIE: An Iterative Querying Approach for Reasoning About Situations
%A Rajagopal, Dheeraj
%A Madaan, Aman
%A Tandon, Niket
%A Yang, Yiming
%A Prabhumoye, Shrimai
%A Ravichander, Abhilasha
%A Clark, Peter
%A Hovy, Eduard H.
%Y Bosselut, Antoine
%Y Li, Xiang
%Y Lin, Bill Yuchen
%Y Shwartz, Vered
%Y Majumder, Bodhisattwa Prasad
%Y Lal, Yash Kumar
%Y Rudinger, Rachel
%Y Ren, Xiang
%Y Tandon, Niket
%Y Zouhar, Vilém
%S Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022)
%D 2022
%8 May
%I Association for Computational Linguistics
%C Dublin, Ireland
%F rajagopal-etal-2022-curie
%X Predicting the effects of unexpected situations is an important reasoning task, e.g., would cloudy skies help or hinder plant growth? Given a context, the goal of such situational reasoning is to elicit the consequences of a new situation (st) that arises in that context. We propose CURIE, a method to iteratively build a graph of relevant consequences explicitly in a structured situational graph (st graph) using natural language queries over a finetuned language model. Across multiple domains, CURIE generates st graphs that humans find relevant and meaningful in eliciting the consequences of a new situation (75% of the graphs were judged correct by humans). We present a case study of a situation reasoning end task (WIQA-QA), where simply augmenting their input with st graphs improves accuracy by 3 points. We show that these improvements mainly come from a hard subset of the data, that requires background knowledge and multi-hop reasoning.
%R 10.18653/v1/2022.csrr-1.7
%U https://aclanthology.org/2022.csrr-1.7
%U https://doi.org/10.18653/v1/2022.csrr-1.7
%P 49-63
Markdown (Informal)
[CURIE: An Iterative Querying Approach for Reasoning About Situations](https://aclanthology.org/2022.csrr-1.7) (Rajagopal et al., CSRR 2022)
ACL
- Dheeraj Rajagopal, Aman Madaan, Niket Tandon, Yiming Yang, Shrimai Prabhumoye, Abhilasha Ravichander, Peter Clark, and Eduard H Hovy. 2022. CURIE: An Iterative Querying Approach for Reasoning About Situations. In Proceedings of the First Workshop on Commonsense Representation and Reasoning (CSRR 2022), pages 49–63, Dublin, Ireland. Association for Computational Linguistics.