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• W2978606418 abstract "In this paper, we propose a simple yet effective framework for multilingual end-to-end speech translation (ST), in which speech utterances in source languages are directly translated to the desired target languages with a universal sequence-to-sequence architecture. While multilingual models have shown to be useful for automatic speech recognition (ASR) and machine translation (MT), this is the first time they are applied to the end-to-end ST problem. We show the effectiveness of multilingual end-to-end ST in two scenarios: one-to-many and many-to-many translations with publicly available data. We experimentally confirm that multilingual end-to-end ST models significantly outperform bilingual ones in both scenarios. The generalization of multilingual training is also evaluated in a transfer learning scenario to a very low-resource language pair. All of our codes and the database are publicly available to encourage further research in this emergent multilingual ST topic." @default.
• W2978606418 created "2019-10-10" @default.
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• W2978606418 date "2019-10-01" @default.
• W2978606418 modified "2023-09-26" @default.
• W2978606418 title "Multilingual End-to-End Speech Translation" @default.
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• W2978606418 doi "https://doi.org/10.48550/arxiv.1910.00254" @default.
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