@inproceedings{madaan-etal-2021-think,
title = "Think about it! Improving defeasible reasoning by first modeling the question scenario.",
author = "Madaan, Aman and
Tandon, Niket and
Rajagopal, Dheeraj and
Clark, Peter and
Yang, Yiming and
Hovy, Eduard",
editor = "Moens, Marie-Francine and
Huang, Xuanjing and
Specia, Lucia and
Yih, Scott Wen-tau",
booktitle = "Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing",
month = nov,
year = "2021",
address = "Online and Punta Cana, Dominican Republic",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2021.emnlp-main.508",
doi = "10.18653/v1/2021.emnlp-main.508",
pages = "6291--6310",
abstract = "Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a {``}mental model{''} of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to {``}think about{''} a question and explicitly model the scenario, rather than answering reflexively.",
}
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<abstract>Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a “mental model” of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to “think about” a question and explicitly model the scenario, rather than answering reflexively.</abstract>
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%0 Conference Proceedings
%T Think about it! Improving defeasible reasoning by first modeling the question scenario.
%A Madaan, Aman
%A Tandon, Niket
%A Rajagopal, Dheeraj
%A Clark, Peter
%A Yang, Yiming
%A Hovy, Eduard
%Y Moens, Marie-Francine
%Y Huang, Xuanjing
%Y Specia, Lucia
%Y Yih, Scott Wen-tau
%S Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
%D 2021
%8 November
%I Association for Computational Linguistics
%C Online and Punta Cana, Dominican Republic
%F madaan-etal-2021-think
%X Defeasible reasoning is the mode of reasoning where conclusions can be overturned by taking into account new evidence. Existing cognitive science literature on defeasible reasoning suggests that a person forms a “mental model” of the problem scenario before answering questions. Our research goal asks whether neural models can similarly benefit from envisioning the question scenario before answering a defeasible query. Our approach is, given a question, to have a model first create a graph of relevant influences, and then leverage that graph as an additional input when answering the question. Our system, CURIOUS, achieves a new state-of-the-art on three different defeasible reasoning datasets. This result is significant as it illustrates that performance can be improved by guiding a system to “think about” a question and explicitly model the scenario, rather than answering reflexively.
%R 10.18653/v1/2021.emnlp-main.508
%U https://aclanthology.org/2021.emnlp-main.508
%U https://doi.org/10.18653/v1/2021.emnlp-main.508
%P 6291-6310
Markdown (Informal)
[Think about it! Improving defeasible reasoning by first modeling the question scenario.](https://aclanthology.org/2021.emnlp-main.508) (Madaan et al., EMNLP 2021)
ACL