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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 – 7 of 7 1 On the Representation Collapse of Sparse Mixture of Experts ... Chi, Zewen; Dong, Li; Huang, Shaohan. - : arXiv, 2022 BASE Show details 2 Allocating Large Vocabulary Capacity for Cross-lingual Language Model Pre-training ... Zheng, Bo; Dong, Li; Huang, Shaohan. - : arXiv, 2021 BASE Show details 3 Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task ... Yang, Jian; Ma, Shuming; Huang, Haoyang. - : arXiv, 2021 BASE Show details 4 DeltaLM: Encoder-Decoder Pre-training for Language Generation and Translation by Augmenting Pretrained Multilingual Encoders ... Ma, Shuming; Dong, Li; Huang, Shaohan. - : arXiv, 2021 BASE Show details 5 XLM-E: Cross-lingual Language Model Pre-training via ELECTRA ... Chi, Zewen; Huang, Shaohan; Dong, Li. - : arXiv, 2021 BASE Show details 6 InfoXLM: An Information-Theoretic Framework for Cross-Lingual Language Model Pre-Training ... Chi, Zewen; Dong, Li; Wei, Furu; Yang, Nan; Singhal, Saksham; Wang, Wenhui; Song, Xia; Mao, Xian-Ling; Huang, Heyan; Zhou, Ming. - : arXiv, 2020 Abstract: In this work, we present an information-theoretic framework that formulates cross-lingual language model pre-training as maximizing mutual information between multilingual-multi-granularity texts. The unified view helps us to better understand the existing methods for learning cross-lingual representations. More importantly, inspired by the framework, we propose a new pre-training task based on contrastive learning. Specifically, we regard a bilingual sentence pair as two views of the same meaning and encourage their encoded representations to be more similar than the negative examples. By leveraging both monolingual and parallel corpora, we jointly train the pretext tasks to improve the cross-lingual transferability of pre-trained models. Experimental results on several benchmarks show that our approach achieves considerably better performance. The code and pre-trained models are available at https://aka.ms/infoxlm. ... : NAACL 2021 ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.2007.07834 https://arxiv.org/abs/2007.07834 BASE Hide details 7 XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders ... Ma, Shuming; Yang, Jian; Huang, Haoyang. - : arXiv, 2020 BASE Show details

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