@inproceedings{kim-etal-2023-towards,
title = "Towards Formality-Aware Neural Machine Translation by Leveraging Context Information",
author = "Kim, Dohee and
Baek, Yujin and
Yang, Soyoung and
Choo, Jaegul",
editor = "Bouamor, Houda and
Pino, Juan and
Bali, Kalika",
booktitle = "Findings of the Association for Computational Linguistics: EMNLP 2023",
month = dec,
year = "2023",
address = "Singapore",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2023.findings-emnlp.494/",
doi = "10.18653/v1/2023.findings-emnlp.494",
pages = "7384--7392",
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."
}
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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)
ACL