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        <title>Exploring Discourse Structure in Document-level Machine Translation</title>
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            <title>Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing</title>
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            <namePart type="given">Houda</namePart>
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            <namePart type="given">Kalika</namePart>
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    <abstract>Neural machine translation has achieved great success in the past few years with the help of transformer architectures and large-scale bilingual corpora. However, when the source text gradually grows into an entire document, the performance of current methods for document-level machine translation (DocMT) is less satisfactory. Although the context is beneficial to the translation in general, it is difficult for traditional methods to utilize such long-range information. Previous studies on DocMT have concentrated on extra contents such as multiple surrounding sentences and input instances divided by a fixed length. We suppose that they ignore the structure inside the source text, which leads to under-utilization of the context. In this paper, we present a more sound paragraph-to-paragraph translation mode and explore whether discourse structure can improve DocMT. We introduce several methods from different perspectives, among which our RST-Att model with a multi-granularity attention mechanism based on the RST parsing tree works best. The experiments show that our method indeed utilizes discourse information and performs better than previous work.</abstract>
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    <identifier type="doi">10.18653/v1/2023.emnlp-main.857</identifier>
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%0 Conference Proceedings
%T Exploring Discourse Structure in Document-level Machine Translation
%A Hu, Xinyu
%A Wan, Xiaojun
%Y Bouamor, Houda
%Y Pino, Juan
%Y Bali, Kalika
%S Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing
%D 2023
%8 December
%I Association for Computational Linguistics
%C Singapore
%F hu-wan-2023-exploring
%X Neural machine translation has achieved great success in the past few years with the help of transformer architectures and large-scale bilingual corpora. However, when the source text gradually grows into an entire document, the performance of current methods for document-level machine translation (DocMT) is less satisfactory. Although the context is beneficial to the translation in general, it is difficult for traditional methods to utilize such long-range information. Previous studies on DocMT have concentrated on extra contents such as multiple surrounding sentences and input instances divided by a fixed length. We suppose that they ignore the structure inside the source text, which leads to under-utilization of the context. In this paper, we present a more sound paragraph-to-paragraph translation mode and explore whether discourse structure can improve DocMT. We introduce several methods from different perspectives, among which our RST-Att model with a multi-granularity attention mechanism based on the RST parsing tree works best. The experiments show that our method indeed utilizes discourse information and performs better than previous work.
%R 10.18653/v1/2023.emnlp-main.857
%U https://aclanthology.org/2023.emnlp-main.857
%U https://doi.org/10.18653/v1/2023.emnlp-main.857
%P 13889-13902

Markdown (Informal)

[Exploring Discourse Structure in Document-level Machine Translation](https://aclanthology.org/2023.emnlp-main.857) (Hu & Wan, EMNLP 2023)

• Exploring Discourse Structure in Document-level Machine Translation (Hu & Wan, EMNLP 2023)

ACL

• Xinyu Hu and Xiaojun Wan. 2023. Exploring Discourse Structure in Document-level Machine Translation. In Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing, pages 13889–13902, Singapore. Association for Computational Linguistics.