Investigating Reasons for Disagreement in Natural Language Inference (2022.tacl-1)
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| Challenge: | Several disagreements in natural language inference (NLI) annotation are due to uncertainty in the sentence meaning, others to annotator biases and task artifacts. |
| Approach: | They propose a 4-way classification approach and a multilabel classification approach for detecting disagreements in natural language inference annotations. |
| Outcome: | The proposed model is more expressive and gives better recall of possible interpretations in the data. |
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