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    <abstract>Current research on quality estimation of machine translation focuses on the sentence-level quality of the translations. By using explainability methods, we can use these quality estimations for word-level error identification. In this work, we compare different explainability techniques and investigate gradient-based and perturbation-based methods by measuring their performance and required computational efforts. Throughout our experiments, we observed that using absolute word scores boosts the performance of gradient-based explainers significantly. Further, we combine explainability methods to ensembles to exploit the strengths of individual explainers to get better explanations. We propose the usage of absolute gradient-based methods. These work comparably well to popular perturbation-based ones while being more time-efficient.</abstract>
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%0 Conference Proceedings
%T Explaining Errors in Machine Translation with Absolute Gradient Ensembles
%A Eksi, Melda
%A Gelbing, Erik
%A Stieber, Jonathan
%A Vu, Chi Viet
%Y Gao, Yang
%Y Eger, Steffen
%Y Zhao, Wei
%Y Lertvittayakumjorn, Piyawat
%Y Fomicheva, Marina
%S Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems
%D 2021
%8 November
%I Association for Computational Linguistics
%C Punta Cana, Dominican Republic
%F eksi-etal-2021-explaining
%X Current research on quality estimation of machine translation focuses on the sentence-level quality of the translations. By using explainability methods, we can use these quality estimations for word-level error identification. In this work, we compare different explainability techniques and investigate gradient-based and perturbation-based methods by measuring their performance and required computational efforts. Throughout our experiments, we observed that using absolute word scores boosts the performance of gradient-based explainers significantly. Further, we combine explainability methods to ensembles to exploit the strengths of individual explainers to get better explanations. We propose the usage of absolute gradient-based methods. These work comparably well to popular perturbation-based ones while being more time-efficient.
%R 10.18653/v1/2021.eval4nlp-1.23
%U https://aclanthology.org/2021.eval4nlp-1.23
%U https://doi.org/10.18653/v1/2021.eval4nlp-1.23
%P 238-249

Markdown (Informal)

[Explaining Errors in Machine Translation with Absolute Gradient Ensembles](https://aclanthology.org/2021.eval4nlp-1.23) (Eksi et al., Eval4NLP 2021)

• Explaining Errors in Machine Translation with Absolute Gradient Ensembles (Eksi et al., Eval4NLP 2021)

ACL

• Melda Eksi, Erik Gelbing, Jonathan Stieber, and Chi Viet Vu. 2021. Explaining Errors in Machine Translation with Absolute Gradient Ensembles. In Proceedings of the 2nd Workshop on Evaluation and Comparison of NLP Systems, pages 238–249, Punta Cana, Dominican Republic. Association for Computational Linguistics.