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All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year AND OR AND NOT All fields Title Creator / Publisher Keyword Year Sort by creator [A → Z]' creator [Z → A]' publishing year ↑ (asc)' publishing year ↓ (desc)' title [A → Z]' title [Z → A]' Simple Search Hits 1 – 6 of 6 1 Facebook AI WMT21 News Translation Task Submission ... Tran, Chau; Bhosale, Shruti; Cross, James. - : arXiv, 2021 BASE Show details 2 Alternative Input Signals Ease Transfer in Multilingual Machine Translation ... Sun, Simeng; Fan, Angela; Cross, James. - : arXiv, 2021 BASE Show details 3 Multilingual Speech Translation from Efficient Finetuning of Pretrained Models ... The Joint Conference of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing 2021; Auli, Michael; Baevski, Alexei. - : Underline Science Inc., 2021 BASE Show details 4 Multilingual Translation with Extensible Multilingual Pretraining and Finetuning ... Tang, Yuqing; Tran, Chau; Li, Xian. - : arXiv, 2020 BASE Show details 5 Cross-lingual Retrieval for Iterative Self-Supervised Training ... Tran, Chau; Tang, Yuqing; Li, Xian. - : arXiv, 2020 BASE Show details 6 Multilingual Speech Translation with Efficient Finetuning of Pretrained Models ... Li, Xian; Wang, Changhan; Tang, Yun; Tran, Chau; Tang, Yuqing; Pino, Juan; Baevski, Alexei; Conneau, Alexis; Auli, Michael. - : arXiv, 2020 Abstract: We present a simple yet effective approach to build multilingual speech-to-text (ST) translation by efficient transfer learning from pretrained speech encoder and text decoder. Our key finding is that a minimalistic LNA (LayerNorm and Attention) finetuning can achieve zero-shot crosslingual and cross-modality transfer ability by only finetuning less than 10% of the pretrained parameters. This enables effectively leveraging large pretrained models with low training cost. Using wav2vec 2.0 for acoustic modeling, and mBART for multilingual text generation, our approach advanced the new state-of-the-art for 34 translation directions (and surpassing cascaded ST for 23 of them) on large-scale multilingual ST benchmark CoVoST 2 (+6.4 BLEU on average across 15 En-X directions and +5.1 BLEU on average across 19 X-En directions). Our approach demonstrates strong zero-shot performance in a many-to-many multilingual model (+5.7 BLEU on average across 18 non-English directions), making it an appealing approach for ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/2010.12829 https://dx.doi.org/10.48550/arxiv.2010.12829 BASE Hide details

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