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        <title>Towards Formality-Aware Neural Machine Translation by Leveraging Context Information</title>
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        <namePart type="given">Dohee</namePart>
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    <name type="personal">
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    <abstract>Formality is one of the most important linguistic properties to determine the naturalness of translation. Although a target-side context contains formality-related tokens, the sparsity within the context makes it difficult for context-aware neural machine translation (NMT) models to properly discern them. In this paper, we introduce a novel training method to explicitly inform the NMT model by pinpointing key informative tokens using a formality classifier. Given a target context, the formality classifier guides the model to concentrate on the formality-related tokens within the context. Additionally, we modify the standard cross-entropy loss, especially toward the formality-related tokens obtained from the classifier. Experimental results show that our approaches not only improve overall translation quality but also reflect the appropriate formality from the target context.</abstract>
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%0 Conference Proceedings
%T Towards Formality-Aware Neural Machine Translation by Leveraging Context Information
%A Kim, Dohee
%A Baek, Yujin
%A Yang, Soyoung
%A Choo, Jaegul
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Findings of the Association for Computational Linguistics: EMNLP 2023
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F kim-etal-2023-towards
%X Formality is one of the most important linguistic properties to determine the naturalness of translation. Although a target-side context contains formality-related tokens, the sparsity within the context makes it difficult for context-aware neural machine translation (NMT) models to properly discern them. In this paper, we introduce a novel training method to explicitly inform the NMT model by pinpointing key informative tokens using a formality classifier. Given a target context, the formality classifier guides the model to concentrate on the formality-related tokens within the context. Additionally, we modify the standard cross-entropy loss, especially toward the formality-related tokens obtained from the classifier. Experimental results show that our approaches not only improve overall translation quality but also reflect the appropriate formality from the target context.
%R 10.18653/v1/2023.findings-emnlp.494
%U https://aclanthology.org/2023.findings-emnlp.494
%U https://doi.org/10.18653/v1/2023.findings-emnlp.494
%P 7384-7392

Markdown (Informal)

[Towards Formality-Aware Neural Machine Translation by Leveraging Context Information](https://aclanthology.org/2023.findings-emnlp.494) (Kim et al., Findings 2023)

• Towards Formality-Aware Neural Machine Translation by Leveraging Context Information (Kim et al., Findings 2023)

ACL

• Dohee Kim, Yujin Baek, Soyoung Yang, and Jaegul Choo. 2023. Towards Formality-Aware Neural Machine Translation by Leveraging Context Information. In Findings of the Association for Computational Linguistics: EMNLP 2023, pages 7384–7392, Singapore. Association for Computational Linguistics.