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Don't Take the Premise for Granted: Mitigating Artifacts in Natural Language Inference
2019
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics
Natural Language Inference (NLI) datasets often contain hypothesis-only biases-artifacts that allow models to achieve non-trivial performance without learning whether a premise entails a hypothesis. We propose two probabilistic methods to build models that are more robust to such biases and better transfer across datasets. In contrast to standard approaches to NLI, our methods predict the probability of a premise given a hypothesis and NLI label, discouraging models from ignoring the premise.
doi:10.18653/v1/p19-1084
dblp:conf/acl/BelinkovPSDR19
fatcat:mhr2mt4olfd77kb673x4ies6jm