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        <title>Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows</title>
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    <abstract>This paper describes SheffieldGATE’s submission to WMT 2024 Chat Shared Translation Task. We participate in three language pairs: English-German, English-Dutch, and English-Portuguese (Brazil). In this work, we introduce a context-aware sliding window decoding method to track dependencies between chat messages. We fine-tune a large pre-trained language model based on the training data provided by the shared task Our experiments (i) compare the model performance between multilingual and bilingual fine-tuning and (ii) assess the impact of different window sizes. Our experimental results demonstrate that utilising contextual information yields superior performance in document-level translation compared to translating documents as isolated text segments, and that models fine-tuned with multilingual data perform better than those fine-tuned with bilingual data.</abstract>
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%0 Conference Proceedings
%T Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows
%A Yang, Xinye
%A Mu, Yida
%A Bontcheva, Kalina
%A Song, Xingyi
%Y Haddow, Barry
%Y Kocmi, Tom
%Y Koehn, Philipp
%Y Monz, Christof
%S Proceedings of the Ninth Conference on Machine Translation
%D 2024
%8 November
%I Association for Computational Linguistics
%C Miami, Florida, USA
%F yang-etal-2024-optimising
%X This paper describes SheffieldGATE’s submission to WMT 2024 Chat Shared Translation Task. We participate in three language pairs: English-German, English-Dutch, and English-Portuguese (Brazil). In this work, we introduce a context-aware sliding window decoding method to track dependencies between chat messages. We fine-tune a large pre-trained language model based on the training data provided by the shared task Our experiments (i) compare the model performance between multilingual and bilingual fine-tuning and (ii) assess the impact of different window sizes. Our experimental results demonstrate that utilising contextual information yields superior performance in document-level translation compared to translating documents as isolated text segments, and that models fine-tuned with multilingual data perform better than those fine-tuned with bilingual data.
%U https://aclanthology.org/2024.wmt-1.101
%P 1004-1010

Markdown (Informal)

[Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows](https://aclanthology.org/2024.wmt-1.101) (Yang et al., WMT 2024)

• Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows (Yang et al., WMT 2024)

ACL

• Xinye Yang, Yida Mu, Kalina Bontcheva, and Xingyi Song. 2024. Optimising LLM-Driven Machine Translation with Context-Aware Sliding Windows. In Proceedings of the Ninth Conference on Machine Translation, pages 1004–1010, Miami, Florida, USA. Association for Computational Linguistics.