  Product matching corresponds to the task of matching identical products
across different data sources. It typically employs available product features
which, apart from being multimodal, i.e., comprised of various data types,
might be non-homogeneous and incomplete. The paper shows that pre-trained,
multilingual Transformer models, after fine-tuning, are suitable for solving
the product matching problem using textual features both in English and Polish
languages. We tested multilingual mBERT and XLM-RoBERTa models in English on
Web Data Commons - training dataset and gold standard for large-scale product
matching. The obtained results show that these models perform similarly to the
latest solutions tested on this set, and in some cases, the results were even
better.
  Additionally, we prepared a new dataset entirely in Polish and based on
offers in selected categories obtained from several online stores for the
research purpose. It is the first open dataset for product matching tasks in
Polish, which allows comparing the effectiveness of the pre-trained models.
Thus, we also showed the baseline results obtained by the fine-tuned mBERT and
XLM-RoBERTa models on the Polish datasets.
