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    <abstract>Recommendation systems, for documents, have become tools for finding relevant content on the Web. However, these systems have limitations when it comes to recommending documents in languages different from the query language, which means they might overlook resources in non-native languages. This research focuses on representing documents across languages by using Transformer Leveraged Document Representations (TLDRs) that are mapped to a cross-lingual domain. Four multilingual pre-trained transformer models (mBERT, mT5 XLM RoBERTa, ErnieM) were evaluated using three mapping methods across 20 language pairs representing combinations of five selected languages of the European Union. Metrics like Mate Retrieval Rate and Reciprocal Rank were used to measure the effectiveness of mapped TLDRs compared to non-mapped ones. The results highlight the power of cross-lingual representations achieved through pre-trained transformers and mapping approaches suggesting a promising direction for expanding beyond language connections, between two specific languages.</abstract>
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%0 Conference Proceedings
%T Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques
%A Tashu, Tsegaye Misikir
%A Kontos, Eduard-Raul
%A Sabatelli, Matthia
%A Valdenegro-Toro, Matias
%S Proceedings of the Second Workshop on Scaling Up Multilingual & Multi-Cultural Evaluation
%D 2025
%8 January
%I Association for Computational Linguistics
%C Abu Dhabi
%F tashu-etal-2025-cross
%X Recommendation systems, for documents, have become tools for finding relevant content on the Web. However, these systems have limitations when it comes to recommending documents in languages different from the query language, which means they might overlook resources in non-native languages. This research focuses on representing documents across languages by using Transformer Leveraged Document Representations (TLDRs) that are mapped to a cross-lingual domain. Four multilingual pre-trained transformer models (mBERT, mT5 XLM RoBERTa, ErnieM) were evaluated using three mapping methods across 20 language pairs representing combinations of five selected languages of the European Union. Metrics like Mate Retrieval Rate and Reciprocal Rank were used to measure the effectiveness of mapped TLDRs compared to non-mapped ones. The results highlight the power of cross-lingual representations achieved through pre-trained transformers and mapping approaches suggesting a promising direction for expanding beyond language connections, between two specific languages.
%U https://aclanthology.org/2025.sumeval-2.4/
%P 39-47

Markdown (Informal)

[Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques](https://aclanthology.org/2025.sumeval-2.4/) (Tashu et al., SUMEval 2025)

• Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques (Tashu et al., SUMEval 2025)

ACL

• Tsegaye Misikir Tashu, Eduard-Raul Kontos, Matthia Sabatelli, and Matias Valdenegro-Toro. 2025. Cross-Lingual Document Recommendations with Transformer-Based Representations: Evaluating Multilingual Models and Mapping Techniques. In Proceedings of the Second Workshop on Scaling Up Multilingual & Multi-Cultural Evaluation, pages 39–47, Abu Dhabi. Association for Computational Linguistics.