Think about it! Improving defeasible reasoning by first modeling the question scenario.

Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, Eduard Hovy


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.
Anthology ID:
2021.emnlp-main.508
Volume:
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
Month:
November
Year:
2021
Address:
Online and Punta Cana, Dominican Republic
Venue:
EMNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6291–6310
Language:
URL:
https://aclanthology.org/2021.emnlp-main.508
DOI:
10.18653/v1/2021.emnlp-main.508
Bibkey:
Cite (ACL):
Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter Clark, Yiming Yang, and Eduard Hovy. 2021. Think about it! Improving defeasible reasoning by first modeling the question scenario.. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 6291–6310, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Cite (Informal):
Think about it! Improving defeasible reasoning by first modeling the question scenario. (Madaan et al., EMNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.emnlp-main.508.pdf
Video:
 https://aclanthology.org/2021.emnlp-main.508.mp4
Code
 madaan/thinkaboutit
Data
ATOMICSNLIWIQA