Investigating Reasons for Disagreement in Natural Language Inference

• Published in: Transactions of the Association for Computational Linguistics Vol. 10; pp. 1357 - 1374
• Main Authors: Jiang, Nan-Jiang, Marneffe, Marie-Catherine de
• Format: Journal Article
• Language: English
• Published: One Broadway, 12th Floor, Cambridge, Massachusetts 02142, USA MIT Press 23.12.2022; MIT Press Journals, The; The MIT Press
• Subjects: Agreements; Annotations; Classification; Datasets; Hypotheses; Inference; Labels; Language; Linguistics; Natural language; Natural language processing; Semantics; Taxonomy; Word sense disambiguation
• ISSN: 2307-387X; 2307-387X
• DOI: 10.1162/tacl_a_00523

We investigate how disagreement in natural language inference (NLI) annotation arises. We developed a taxonomy of disagreement sources with 10 categories spanning 3 high- level 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.