Investigating Reasons for Disagreement in Natural Language Inference
نویسندگان
چکیده
Abstract We investigate how disagreement in natural language inference (NLI) annotation arises. developed a taxonomy of sources with 10 categories spanning 3 high- level classes. found that some disagreements are due to uncertainty the sentence meaning, others annotator biases and task artifacts, leading different interpretations label distribution. explore two modeling approaches for detecting items potential disagreement: 4-way classification “Complicated” addition three standard NLI labels, multilabel approach. is more expressive gives better recall possible data.
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ژورنال
عنوان ژورنال: Transactions of the Association for Computational Linguistics
سال: 2022
ISSN: ['2307-387X']
DOI: https://doi.org/10.1162/tacl_a_00523