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- W4294619053 abstract "Hierarchical attention networks have recently achieved remarkable performance for document classification in a given language. However, when multilingual document collections are considered, training such models separately for each language entails linear parameter growth and lack of cross-language transfer. Learning a single multilingual model with fewer parameters is therefore a challenging but potentially beneficial objective. To this end, we propose multilingual hierarchical attention networks for learning document structures, with shared encoders and/or shared attention mechanisms across languages, using multi-task learning and an aligned semantic space as input. We evaluate the proposed models on multilingual document classification with disjoint label sets, on a large dataset which we provide, with 600k news documents in 8 languages, and 5k labels. The multilingual models outperform monolingual ones in low-resource as well as full-resource settings, and use fewer parameters, thus confirming their computational efficiency and the utility of cross-language transfer." @default.
- W4294619053 created "2022-09-05" @default.
- W4294619053 creator A5044462851 @default.
- W4294619053 creator A5084740578 @default.
- W4294619053 date "2017-07-04" @default.
- W4294619053 modified "2023-10-18" @default.
- W4294619053 title "Multilingual Hierarchical Attention Networks for Document Classification" @default.
- W4294619053 doi "https://doi.org/10.48550/arxiv.1707.00896" @default.
- W4294619053 hasPublicationYear "2017" @default.
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