A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference

• URL: http://arxiv.org/abs/2204.05428v2
• Date: Mon, 5 Jun 2023 00:14:19 GMT
• Title: A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference
• Authors: Kerem Zaman, Yonatan Belinkov
• Abstract summary: We present a multilingual approach for evaluating attribution methods for the Natural Language Inference (NLI) task in terms of faithfulness and plausibility. First, we introduce a novel cross-lingual strategy to measure faithfulness based on word alignments, which eliminates the drawbacks of erasure-based evaluations. We then perform a comprehensive evaluation of attribution methods, considering different output mechanisms and aggregation methods.
• Score: 28.949004915740776
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Most evaluations of attribution methods focus on the English language. In this work, we present a multilingual approach for evaluating attribution methods for the Natural Language Inference (NLI) task in terms of faithfulness and plausibility. First, we introduce a novel cross-lingual strategy to measure faithfulness based on word alignments, which eliminates the drawbacks of erasure-based evaluations.We then perform a comprehensive evaluation of attribution methods, considering different output mechanisms and aggregation methods. Finally, we augment the XNLI dataset with highlight-based explanations, providing a multilingual NLI dataset with highlights, to support future exNLP studies. Our results show that attribution methods performing best for plausibility and faithfulness are different.

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