# ConECT Dataset: Overcoming Data Scarcity in Context-Aware E-Commerce MT

arXiv: https://arxiv.org/abs/2506.04929 · HTML: https://mlsanity.com/paper/2506.04929/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 25 of its 41 references that are in the map.
- First posted on arXiv 5 Jun 2025.

## Builds on (most cited first, 25 of 25)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [Training language models to follow instructions with human feedback](https://mlsanity.com/paper/2203.02155/) (2022)
- [LoRA: Low-Rank Adaptation of Large Language Models](https://mlsanity.com/paper/2106.09685/) (2021)
- [LLaMA: Open and Efficient Foundation Language Models](https://mlsanity.com/paper/2302.13971/) (2023)
- [Neural Machine Translation by Jointly Learning to Align and Translate](https://mlsanity.com/paper/1409.0473/) (2014)
- [SentencePiece: A simple and language independent subword tokenizer and detokenizer for Neural Text Processing](https://mlsanity.com/paper/1808.06226/) (2018)
- [A Call for Clarity in Reporting BLEU Scores](https://mlsanity.com/paper/1804.08771/) (2018)
- [PaliGemma: A versatile 3B VLM for transfer](https://mlsanity.com/paper/2407.07726/) (2024)
- [Multi30K: Multilingual English-German Image Descriptions](https://mlsanity.com/paper/1605.00459/) (2016)
- [Marian: Fast Neural Machine Translation in C++](https://mlsanity.com/paper/1804.00344/) (2018)
- [Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description](https://mlsanity.com/paper/1710.07177/) (2017)
- [A Novel Graph-based Multi-modal Fusion Encoder for Neural Machine Translation](https://mlsanity.com/paper/2007.08742/) (2020)
- [Doubly-Attentive Decoder for Multi-modal Neural Machine Translation](https://mlsanity.com/paper/1702.01287/) (2017)
- [Incorporating Global Visual Features into Attention-Based Neural Machine Translation](https://mlsanity.com/paper/1701.06521/) (2017)
- [Visual Grounding in Video for Unsupervised Word Translation](https://mlsanity.com/paper/2003.05078/) (2020)
- [Tackling Ambiguity with Images: Improved Multimodal Machine Translation and Contrastive Evaluation](https://mlsanity.com/paper/2212.10140/) (2022)
- [Gumbel-Attention for Multi-modal Machine Translation](https://mlsanity.com/paper/2103.08862/) (2021)
- [Multimodal Attention for Neural Machine Translation](https://mlsanity.com/paper/1609.03976/) (2016)
- [Unsupervised Multi-modal Neural Machine Translation](https://mlsanity.com/paper/1811.11365/) (2018)
- [Efficient Object-Level Visual Context Modeling for Multimodal Machine Translation: Masking Irrelevant Objects Helps Grounding](https://mlsanity.com/paper/2101.05208/) (2020)
- [Product-oriented Machine Translation with Cross-modal Cross-lingual Pre-training](https://mlsanity.com/paper/2108.11119/) (2021)
- [Visual Agreement Regularized Training for Multi-Modal Machine Translation](https://mlsanity.com/paper/1912.12014/) (2019)
- [From Words to Sentences: A Progressive Learning Approach for Zero-resource Machine Translation with Visual Pivots](https://mlsanity.com/paper/1906.00872/) (2019)
- [Towards Zero-Shot Multimodal Machine Translation](https://mlsanity.com/paper/2407.13579/) (2024)

## Built on by (most cited first, 1 of 1)

- [Mix-Ecom: Towards Mixed-Type E-Commerce Dialogues with Complex Domain Rules](https://mlsanity.com/paper/2509.23836/) (2025)

## Abstract (arXiv)

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.