Cross-language learning for product matching


Peeters, Ralph ; Bizer, Christian


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DOI: https://doi.org/10.1145/3487553.3524234
URL: https://dl.acm.org/doi/abs/10.1145/3487553.3524234
URN: urn:nbn:de:bsz:180-madoc-626623
Document Type: Conference or workshop publication
Year of publication: 2022
Book title: Companion Proceedings of the Web Conference 2022
Page range: 236-238
Conference title: WWW '22
Location of the conference venue: Lyon, France, Online
Date of the conference: 25.-29.04.2022
Publisher: Laforest, Frédérique ; Troncy, Raphaël
Place of publication: New York, NY
Publishing house: ACM
ISBN: 978-1-4503-9130-6
Related URLs:
Publication language: English
Institution: School of Business Informatics and Mathematics > Wirtschaftsinformatik V (Bizer)
Pre-existing license: Creative Commons Attribution 4.0 International (CC BY 4.0)
Subject: 004 Computer science, internet
Classification: CCS: Information systems → Entity resolution; Data extraction and integration,
Keywords (English): entity matching , cross-language learning , e-commerce , transformers , schema.org
Abstract: Transformer-based entity matching methods have significantly moved the state of the art for less-structured matching tasks such as matching product offers in e-commerce. In order to excel at these tasks, Transformer-based matching methods require a decent amount of training pairs. Providing enough training data can be challenging, especially if a matcher for non-English product descriptions should be learned. This poster explores along the use case of matching product offers from different e-shops to which extent it is possible to improve the performance of Transformer-based matchers by complementing a small set of training pairs in the target language, German in our case, with a larger set of English-language training pairs. Our experiments using different Transformers show that extending the German set with English pairs improves the matching performance in all cases. The impact of adding the English pairs is especially high in low-resource settings in which only a rather small number of non-English pairs is available. As it is often possible to automatically gather English training pairs from the Web by exploiting schema.org annotations, our results are relevant for many product matching scenarios targeting low-resource languages.

Dieser Eintrag ist Teil der Universitätsbibliographie.

Das Dokument wird vom Publikationsserver der Universitätsbibliothek Mannheim bereitgestellt.




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