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        <title>Entity-aware Multi-task Training Helps Rare Word Machine Translation</title>
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            <namePart type="given">Saad</namePart>
            <namePart type="family">Mahamood</namePart>
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            <namePart type="given">Daphne</namePart>
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    <abstract>Named entities (NE) are integral for preserving context and conveying accurate information in the machine translation (MT) task. Challenges often lie in handling NE diversity, ambiguity, rarity, and ensuring alignment and consistency. In this paper, we explore the effect of NE-aware model fine-tuning to improve handling of NEs in MT. We generate data for NE recognition (NER) and NE-aware MT using common NER tools from Spacy, and align entities in parallel data. Experiments with fine-tuning variations of pre-trained T5 models on NE-related generation tasks between English and German show promising results with increasing amounts of NEs in the output and BLEU score improvements compared to the non-tuned baselines.</abstract>
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%0 Conference Proceedings
%T Entity-aware Multi-task Training Helps Rare Word Machine Translation
%A Rikters, Matiss
%A Miwa, Makoto
%Y Mahamood, Saad
%Y Minh, Nguyen Le
%Y Ippolito, Daphne
%S Proceedings of the 17th International Natural Language Generation Conference
%D 2024
%8 September
%I Association for Computational Linguistics
%C Tokyo, Japan
%F rikters-miwa-2024-entity-aware
%X Named entities (NE) are integral for preserving context and conveying accurate information in the machine translation (MT) task. Challenges often lie in handling NE diversity, ambiguity, rarity, and ensuring alignment and consistency. In this paper, we explore the effect of NE-aware model fine-tuning to improve handling of NEs in MT. We generate data for NE recognition (NER) and NE-aware MT using common NER tools from Spacy, and align entities in parallel data. Experiments with fine-tuning variations of pre-trained T5 models on NE-related generation tasks between English and German show promising results with increasing amounts of NEs in the output and BLEU score improvements compared to the non-tuned baselines.
%U https://aclanthology.org/2024.inlg-main.5
%P 47-54

Markdown (Informal)

[Entity-aware Multi-task Training Helps Rare Word Machine Translation](https://aclanthology.org/2024.inlg-main.5) (Rikters & Miwa, INLG 2024)

• Entity-aware Multi-task Training Helps Rare Word Machine Translation (Rikters & Miwa, INLG 2024)

ACL

• Matiss Rikters and Makoto Miwa. 2024. Entity-aware Multi-task Training Helps Rare Word Machine Translation. In Proceedings of the 17th International Natural Language Generation Conference, pages 47–54, Tokyo, Japan. Association for Computational Linguistics.