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        <title>The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks</title>
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        <namePart type="given">Maria</namePart>
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        <namePart type="given">Prashanth</namePart>
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        <namePart type="given">Rejwanul</namePart>
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    <abstract>This paper presents the SETU-ADAPT submissions to the WMT24 Chat Translation Task. Large language models (LLM) currently provides the state-of-the-art solutions in many natural language processing (NLP) problems including machine translation (MT). For the WMT24 Chat Translation Task we leveraged LLMs for their MT capabilities. In order to adapt the LLMs for a specific domain of interest, we explored different fine-tuning and prompting strategies. We also employed efficient data retrieval methods to curate the data used for fine-tuning. We carried out experiments for two language pairs: German-to-English and French-to-English. Our MT models were evaluated using three metrics: BLEU, chrF and COMET. In this paper we describes our experiments including training setups, results and findings.</abstract>
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%0 Conference Proceedings
%T The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks
%A Zafar, Maria
%A Castaldo, Antonio
%A Nayak, Prashanth
%A Haque, Rejwanul
%A Way, Andy
%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 zafar-etal-2024-setu-adapt
%X This paper presents the SETU-ADAPT submissions to the WMT24 Chat Translation Task. Large language models (LLM) currently provides the state-of-the-art solutions in many natural language processing (NLP) problems including machine translation (MT). For the WMT24 Chat Translation Task we leveraged LLMs for their MT capabilities. In order to adapt the LLMs for a specific domain of interest, we explored different fine-tuning and prompting strategies. We also employed efficient data retrieval methods to curate the data used for fine-tuning. We carried out experiments for two language pairs: German-to-English and French-to-English. Our MT models were evaluated using three metrics: BLEU, chrF and COMET. In this paper we describes our experiments including training setups, results and findings.
%U https://aclanthology.org/2024.wmt-1.104
%P 1023-1030

Markdown (Informal)

[The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks](https://aclanthology.org/2024.wmt-1.104) (Zafar et al., WMT 2024)

• The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks (Zafar et al., WMT 2024)

ACL

• Maria Zafar, Antonio Castaldo, Prashanth Nayak, Rejwanul Haque, and Andy Way. 2024. The SETU-ADAPT Submissions to WMT 2024 Chat Translation Tasks. In Proceedings of the Ninth Conference on Machine Translation, pages 1023–1030, Miami, Florida, USA. Association for Computational Linguistics.