The DOI matches its Crossref record; 52 of the 52 references it cites were found in a registry or an index.
Multilingual Denoising Pre-training for Neural Machine Translation
Open accessAbstract
This paper demonstrates that multilingual denoising pre-training produces significant performance gains across a wide variety of machine translation (MT) tasks. We present mBART—a sequence-to-sequence denoising auto-encoder pre-trained on large-scale monolingual corpora in many languages using the BART objective (Lewis et al., 2019 ). mBART is the first method for pre-training a complete sequence-to-sequence model by denoising full texts in multiple languages, whereas previous approaches have focused only on the encoder, decoder, or reconstructing parts of the text. Pre-training a complete model allows it to be directly fine-tuned for supervised (both sentence-level and document-level) and unsupervised machine translation, with no task- specific modifications. We demonstrate that adding mBART initialization produces performance gains in all but the highest-resource settings, including up to 12 BLEU points for low resource MT and over 5 BLEU points for many document-level and unsupervised models. We also show that it enables transfer to language pairs with no bi-text or that were not in the pre-training corpus, and present extensive analysis of which factors contribute the most to effective pre-training. 1
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BLEU: a method for automatic evaluation of machine translation
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RoBERTa: A Robustly Optimized BERT Pretraining Approach
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Metadata
- Authors
- Liu, Y., Gu, J., Goyal, N., Li, X., Edunov, S., Ghazvininejad, M., Lewis, M., & Zettlemoyer, L.
- Venue
- Transactions of the Association for Computational Linguistics
- Publisher
- MIT Press - Journals
- Published
- 2020-12
- Volume
- 8
- Pages
- 726-742
- ISSN
- 2307-387X (ISSN-L)
- OpenAlex
- W3001434439
- Cited by
- 1,527
- References
- 52
- FWCI
- 93.76
- Type
- journal article
- Language
- en
- Open access
- diamond
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- Liu, Y., Gu, J., Goyal, N., Li, X., Edunov, S., Ghazvininejad, M., Lewis, M., & Zettlemoyer, L. (2020). Multilingual Denoising Pre-training for Neural Machine Translation. Transactions of the Association for Computational Linguistics. https://doi.org/10.1162/tacl_a_00343
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