Investigating Reasons for Disagreement in Natural Language Inference

Autor:	Jiang, Nan-Jiang, de Marneffe, Marie-Catherine
Rok vydání:	2022
Předmět:	Computer Science - Computation and Language
Druh dokumentu:	Working Paper
Popis:	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. Comment: accepted at TACL, pre-MIT Press publication version
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2209.03392 Zobrazit plný text záznamu View this record from Arxiv