Nov 01, 2020 · Liu et al. presented mBART (Multilingual BART, which is an extension of the original BART 19) a denoising auto-encoder that they pre-trained on several monolingual language corpora. They have shown that using mBART has led to an increase in performance of up to 12 BLEU points for some low resource sentence-level translation tasks and an ...
Fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python. ... (e.g. Bart, ProphetNet) for text generation, summarization, translation tasks etc.

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Gpt2 Translation Facebook AI首次提出多語言機器翻譯(MMT)模型——M2M -100,該模型可以在不依賴英語數據的情況下在任何100種語言間進行翻譯。 該項目已開源。

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Fairseq(-py) is a sequence modeling toolkit that allows researchers and developers to train custom models for translation, summarization, language modeling and other text generation tasks. We provide reference implementations of various sequence modeling papers: List of implemented papers.

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For BART model implementation, we use fairseq toolkit Ott et al. implementation of BART. Additionally, we used bart_large model weights 4 . Since BART model already contains < m a s k > token, we use it to replace mask words.

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use the large pre-trained BART model (Lewis et al., 2019) and fine-tune with the proposed method. Ex-periments on real news articles show our approach achieves performance boosts over existing meth-ods. When adapting to the previous synthetic do-main (Frermann and Klementiev,2019), the BART model after fine-tuning with our weak supervisions

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Table 2: Dev set results on benchmark natural language understanding tasks. The RoBERTa-base model here is pretrained with same corpus as BERT. - "Linformer: Self-Attention with Linear Complexity" Fairseq example ... Fairseq example

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Fairseq - Facebook AI Research Sequence-to-Sequence Toolkit written in Python. ... (e.g. Bart, ProphetNet) for text generation, summarization, translation tasks etc.

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