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Divide and Rule: Effective Pre-Training for Context-Aware Multi-Encoder Translation Models.

, , and . ACL (1), page 4557-4572. Association for Computational Linguistics, (2022)

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Actes de la conférence conjointe JEP-TALN-RECITAL 2012, volume 4: conférences invités, , , , and . ATALA/AFCP, (2012)Nonlinear acoustical preprocessing for multiple sampling rates ASR and ASR in noisy condition., and . NOLISP, page 2. ISCA, (2003)Automatic Identification of Arabic Dialects., , and . LREC, European Language Resources Association, (2010)Using closely-related language to build an ASR for a very under-resourced language: Iban., , , and . O-COCOSDA, page 1-5. IEEE, (2014)Character-based NMT with Transformer., , , and . CoRR, (2019)Investigating the role of machine translated text in ASR domain adaptation: Unsupervised and semi-supervised methods., , , and . ASRU, page 260-265. IEEE, (2011)Unwritten languages demand attention too! Word discovery with encoder-decoder models., , , and . ASRU, page 458-465. IEEE, (2017)Step-by-step and integrated approaches in broadcast news speaker diarization., , , , and . Comput. Speech Lang., 20 (2-3): 303-330 (2006)Préface., and . Trait. Autom. des Langues, 55 (2): 7-11 (2014)Divide and Rule: Training Context-Aware Multi-Encoder Translation Models with Little Resources., , and . CoRR, (2021)