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 (translated)
神经机器翻译（NMT）通过使用基于Transformer的模型在翻译方面取得了进步，但仍面临词汇歧义和上下文理解的问题。这一问题在特定领域的应用中尤为关键，这些领域常常遇到表述不清或数据质量差的情况。我们的研究探讨了如何通过向模型添加信息来提升电子商务数据中的翻译质量。为此，我们创建了ConECT——一个新的从捷克语到波兰语的电子商务产品翻译数据集，并与图像和产品元数据相结合，包含11,400对句子。接着，我们调查并比较了不同的适用于上下文感知翻译的方法。 我们在测试中使用了一种视觉-语言模型（VLM），发现视觉信息有助于提升翻译质量。此外，我们也探索了将上下文信息整合进文本到文本模型中的方法，比如产品的类别路径或图像描述的集成。研究结果表明，通过引入上下文信息能够显著提高机器翻译的质量。 我们计划公开发布这一新数据集。
URL
https://arxiv.org/abs/2506.04929
