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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 – 3 of 3 1 Multilingual Translation via Grafting Pre-trained Language Models ... Sun, Zewei; Wang, Mingxuan; Li, Lei. - : arXiv, 2021 BASE Show details 2 Multilingual Translation via Grafting Pre-trained Language Models ... The 2021 Conference on Empirical Methods in Natural Language Processing 2021; Li, Lei; Sun, Zewei; Wang, Mingxuan. - : Underline Science Inc., 2021 Abstract: Can pre-trained BERT for one language and GPT for another be glued together to translate texts? Self-supervised training using only monolingual data has led to the success of pre-trained (masked) language models in many NLP tasks. However, directly connecting BERT as an encoder and GPT as a decoder can be challenging in machine translation, for GPT-like models lack a cross-attention component that is needed in seq2seq decoders. In this paper, we propose Graformer to graft separately pre-trained (masked) language models for machine translation. With monolingual data for pre-training and parallel data for grafting training, we maximally take advantage of the usage of both types of data. Experiments on 60 directions show that our method achieves average improvements of 5.8 BLEU in x2en and 2.9 BLEU in en2x directions comparing with the multilingual Transformer of the same size. ... URL: https://dx.doi.org/10.48448/s1t4-ez34 https://underline.io/lecture/38416-multilingual-translation-via-grafting-pre-trained-language-models BASE Hide details 3 Rethinking Document-level Neural Machine Translation ... Sun, Zewei; Wang, Mingxuan; Zhou, Hao. - : arXiv, 2020 BASE Show details

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