Evaluating resource-lean cross-lingual embedding models in unsupervised retrieval
| Autor: | Robert Litschko, Goran Glavaš, Laura Dietz, Ivan Vulić |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Cross lingual
Machine translation business.industry Computer science Cross-Lingual IR InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL 02 engineering and technology computer.software_genre Cross-Lingual Embeddings CLIR Evaluation Variety (cybernetics) 03 medical and health sciences 0302 clinical medicine Resource (project management) 030221 ophthalmology & optometry 0202 electrical engineering electronic engineering information engineering Embedding 020201 artificial intelligence & image processing Artificial intelligence business Projection (set theory) computer Word (computer architecture) Natural language processing |
| Zdroj: | SIGIR Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval Laura Dietz Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval-SIGIR19 Proceedings of the 42nd International ACM SIGIR Conference on Research and Development in Information Retrieval -SIGIR'19 |
| DOI: | 10.17863/cam.45164 |
| Popis: | Cross-lingual embeddings (CLE) facilitate cross-lingual natural language processing and information retrieval. Recently, a wide variety of resource-lean projection-based models for inducing CLEs has been introduced, requiring limited or no bilingual supervision. Despite potential usefulness in downstream IR and NLP tasks, these CLE models have almost exclusively been evaluated on word translation tasks. In this work, we provide a comprehensive comparative evaluation of projection-based CLE models for both sentence-level and document-level cross-lingual Information Retrieval (CLIR). We show that in some settings resource-lean CLE-based CLIR models may outperform resource-intensive models using full-blown machine translation (MT). We hope our work serves as a guideline for choosing the right model for CLIR practitioners. |
| Databáze: | OpenAIRE |
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