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Modern Neural Machine Translation systems exhibit strong performance in several different languages and are constantly improving.
Three scenarios for continual learning
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Thomas Wolf, Lysandre Debut, Victor Sanh, Julien Chaumond, Clement Delangue, Anthony Moi, Pierric Cistac, Tim Rault, Rémi Louf, Morgan Funtowicz, and Jamie Brew. 2019 · 1910
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Regularization shortcomings for continual learning
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Continual learning for neural machine translation
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Beyond english-centric multilingual machine translation
Angela Fan, Shruti Bhosale, Holger Schwenk, Zhiyi Ma, Ahmed El-Kishky, Siddharth Goyal, Mandeep Baines, Onur Celebi, Guillaume Wenzek, Vishrav Chaudhary, Naman Goyal, Tom Birch, Vitaliy Liptchinsky, Sergey Edunov, Michael Auli, and Armand Joulin. 2021 · 2021
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Pruning-then-expanding model for domain adaptation of neural machine translation
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Parameter-efficient transfer learning for NLP
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Overcoming catastrophic forgetting during domain adaptation of seq2seq language generation
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CLLE: A benchmark for continual language learning evaluation in multilingual machine translation
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Continual sequence generation with adaptive compositional modules
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Overcoming catastrophic forgetting beyond continual learning: Balanced training for neural machine translation
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Overcoming catastrophic forgetting during domain adaptation of neural machine translation
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