Generating Intermediate Steps for NLI with Next-Step Supervision
- URL: http://arxiv.org/abs/2208.14641v1
- Date: Wed, 31 Aug 2022 05:25:33 GMT
- Title: Generating Intermediate Steps for NLI with Next-Step Supervision
- Authors: Deepanway Ghosal and Somak Aditya and Monojit Choudhury
- Abstract summary: We train a sequence-to-sequence model to generate only the next step given an NLI premise and hypothesis pair.
We then enhance it with external knowledge and symbolic search to generate intermediate steps with only next-step supervision.
We show the correctness of such generated steps through automated and human verification.
- Score: 15.425765421938447
- License: http://creativecommons.org/licenses/by-nc-sa/4.0/
- Abstract: The Natural Language Inference (NLI) task often requires reasoning over
multiple steps to reach the conclusion. While the necessity of generating such
intermediate steps (instead of a summary explanation) has gained popular
support, it is unclear how to generate such steps without complete end-to-end
supervision and how such generated steps can be further utilized. In this work,
we train a sequence-to-sequence model to generate only the next step given an
NLI premise and hypothesis pair (and previous steps); then enhance it with
external knowledge and symbolic search to generate intermediate steps with only
next-step supervision. We show the correctness of such generated steps through
automated and human verification. Furthermore, we show that such generated
steps can help improve end-to-end NLI task performance using simple data
augmentation strategies, across multiple public NLI datasets.
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