Investigating Reasons for Disagreement in Natural Language Inference

Jiang, Nan-Jiang;de Marneffe, Marie-Catherine
(2022) Transactions of the Association for Computational Linguistics — Vol. 10, p. 1357-1374 (2022)

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We investigate how disagreement in natural language inference (NLI) annotation arises. We developed a taxonomy of disagreement sources with 10 categories spanning 3 highlevel classes. We found that some disagreements are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts, leading to different interpretations of the label distribution. We explore two modeling approaches for detecting items with potential disagreement: a 4-way classification with a ‘‘Complicated’’ label in addition to the three standard NLI labels, and a multilabel classification approach. We found that the multilabel classification is more expressive and gives better recall of the possible interpretations in the data.
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Jiang, N.-J., & de Marneffe, M.-C. (2022). Investigating Reasons for Disagreement in Natural Language Inference. Transactions of the Association for Computational Linguistics, 10, 1357-1374. https://doi.org/10.1162/tacl_a_00523 (Original work published 2022)