Think about it! Improving defeasible reasoning by first modeling the question scenario.
Aman Madaan, Niket Tandon, Dheeraj Rajagopal, Peter E. Clark, Yiming Yang, Eduard H. Hovy · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing · 2021
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 stateof-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. 1