ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT
Journal:
arXiv
Published Date:
Jun 5, 2025
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.
