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    <abstract>Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, which often have problems with unclear sentences or poor data quality. Our research explores how adding information to models can improve translations in the context of e-commerce data. To this end we create ConECT– a new Czech-to-Polish e-commerce product translation dataset coupled with images and product metadata consisting of 11,400 sentence pairs. We then investigate and compare different methods that are applicable to context-aware translation. We test a vision-language model (VLM), finding that visual context aids translation quality. Additionally, we explore the incorporation of contextual information into text-to-text models, such as the product’s category path or image descriptions. The results of our study demonstrate that the incorporation of contextual information leads to an improvement in the quality of machine translation. We make the new dataset publicly available.</abstract>
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%0 Conference Proceedings
%T ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
%A Pokrywka, Mikołaj
%A Kusa, Wojciech
%A Rutkowski, Mieszko
%A Koszowski, Mikołaj
%Y Che, Wanxiang
%Y Nabende, Joyce
%Y Shutova, Ekaterina
%Y Pilehvar, Mohammad Taher
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-252-7
%F pokrywka-etal-2025-conect
%X Neural Machine Translation (NMT) has improved translation by using Transformer-based models, but it still struggles with word ambiguity and context. This problem is especially important in domain-specific applications, which often have problems with unclear sentences or poor data quality. Our research explores how adding information to models can improve translations in the context of e-commerce data. To this end we create ConECT– a new Czech-to-Polish e-commerce product translation dataset coupled with images and product metadata consisting of 11,400 sentence pairs. We then investigate and compare different methods that are applicable to context-aware translation. We test a vision-language model (VLM), finding that visual context aids translation quality. Additionally, we explore the incorporation of contextual information into text-to-text models, such as the product’s category path or image descriptions. The results of our study demonstrate that the incorporation of contextual information leads to an improvement in the quality of machine translation. We make the new dataset publicly available.
%R 10.18653/v1/2025.acl-short.7
%U https://aclanthology.org/2025.acl-short.7/
%U https://doi.org/10.18653/v1/2025.acl-short.7
%P 79-86

Markdown (Informal)

[ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT](https://aclanthology.org/2025.acl-short.7/) (Pokrywka et al., ACL 2025)

• ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT (Pokrywka et al., ACL 2025)

ACL

• Mikołaj Pokrywka, Wojciech Kusa, Mieszko Rutkowski, and Mikołaj Koszowski. 2025. ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers), pages 79–86, Vienna, Austria. Association for Computational Linguistics.