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Cross-Lingual Learning-to-Rank with Shared Representations
2018
Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 2 (Short Papers)
Cross-lingual information retrieval (CLIR) is a document retrieval task where the documents are written in a language different from that of the user's query. This is a challenging problem for data-driven approaches due to the general lack of labeled training data. We introduce a large-scale dataset derived from Wikipedia to support CLIR research in 25 languages. Further, we present a simple yet effective neural learning-to-rank model that shares representations across languages and reduces the
doi:10.18653/v1/n18-2073
dblp:conf/naacl/SasakiSSDI18
fatcat:mrdjzvlqgnda5bu74vzlg4xwwi