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arXiv · 1811.12112

Non-entailed subsequences as a challenge for natural language inference

R. Thomas McCoy·Tal Linzen
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Abstract

Neural network models have shown great success at natural language inference (NLI), the task of determining whether a premise entails a hypothesis. However, recent studies suggest that these models may rely on fallible heuristics rather than deep language understanding. We introduce a challenge set to test whether NLI systems adopt one such heuristic: assuming that a sentence entails all of its subsequences, such as assuming that "Alice believes Mary is lying" entails "Alice believes Mary." We evaluate several competitive NLI models on this challenge set and find strong evidence that they do rely on the subsequence heuristic.

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R. Thomas McCoy, Tal Linzen. 2018-11-29. Non-entailed subsequences as a challenge for natural language inference. https://arxiv.org/abs/1811.12112

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