Think about it! Improving defeasible reasoning by first modeling the question scenario
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 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. Code, data,
and pre-trained models are located at https://github.com/madaan/thinkaboutit.
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