@inproceedings{zaman-belinkov-2022-multilingual,
title = "A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference",
author = "Zaman, Kerem and
Belinkov, Yonatan",
editor = "Goldberg, Yoav and
Kozareva, Zornitsa and
Zhang, Yue",
booktitle = "Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing",
month = dec,
year = "2022",
address = "Abu Dhabi, United Arab Emirates",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2022.emnlp-main.101",
doi = "10.18653/v1/2022.emnlp-main.101",
pages = "1556--1576",
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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%0 Conference Proceedings
%T A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference
%A Zaman, Kerem
%A Belinkov, Yonatan
%Y Goldberg, Yoav
%Y Kozareva, Zornitsa
%Y Zhang, Yue
%S Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
%D 2022
%8 December
%I Association for Computational Linguistics
%C Abu Dhabi, United Arab Emirates
%F zaman-belinkov-2022-multilingual
%X 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.
%R 10.18653/v1/2022.emnlp-main.101
%U https://aclanthology.org/2022.emnlp-main.101
%U https://doi.org/10.18653/v1/2022.emnlp-main.101
%P 1556-1576
Markdown (Informal)
[A Multilingual Perspective Towards the Evaluation of Attribution Methods in Natural Language Inference](https://aclanthology.org/2022.emnlp-main.101) (Zaman & Belinkov, EMNLP 2022)
ACL