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- W4320448004 abstract "Recent results show that deep neural networks using contextual embeddings significantly outperform non-contextual embeddings on a majority of text classification task. We offer precomputed embeddings from popular contextual ELMo model for seven languages: Croatian, Estonian, Finnish, Latvian, Lithuanian, Slovenian, and Swedish. We demonstrate that the quality of embeddings strongly depends on the size of training set and show that existing publicly available ELMo embeddings for listed languages shall be improved. We train new ELMo embeddings on much larger training sets and show their advantage over baseline non-contextual FastText embeddings. In evaluation, we use two benchmarks, the analogy task and the NER task." @default.
- W4320448004 created "2023-02-14" @default.
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- W4320448004 date "2019-11-22" @default.
- W4320448004 modified "2023-09-28" @default.
- W4320448004 title "High Quality ELMo Embeddings for Seven Less-Resourced Languages" @default.
- W4320448004 doi "https://doi.org/10.48550/arxiv.1911.10049" @default.
- W4320448004 hasPublicationYear "2019" @default.
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