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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 – 16 of 16 1 SMDT: Selective Memory-Augmented Neural Document Translation ... Zhang, Xu; Yang, Jian; Huang, Haoyang. - : arXiv, 2022 BASE Show details 2 StableMoE: Stable Routing Strategy for Mixture of Experts ... Dai, Damai; Dong, Li; Ma, Shuming. - : arXiv, 2022 BASE Show details 3 DeepNet: Scaling Transformers to 1,000 Layers ... Wang, Hongyu; Ma, Shuming; Dong, Li. - : arXiv, 2022 BASE Show details 4 Zero-shot Cross-lingual Transfer of Prompt-based Tuning with a Unified Multilingual Prompt ... Huang, Lianzhe; Ma, Shuming; Zhang, Dongdong. - : arXiv, 2022 BASE Show details 5 On the Representation Collapse of Sparse Mixture of Experts ... Chi, Zewen; Dong, Li; Huang, Shaohan. - : arXiv, 2022 BASE Show details 6 A Unified Strategy for Multilingual Grammatical Error Correction with Pre-trained Cross-Lingual Language Model ... Sun, Xin; Ge, Tao; Ma, Shuming. - : arXiv, 2022 BASE Show details 7 Towards Making the Most of Multilingual Pretraining for Zero-Shot Neural Machine Translation ... Chen, Guanhua; Ma, Shuming; Chen, Yun. - : arXiv, 2021 BASE Show details 8 Zero-shot Cross-lingual Transfer of Neural Machine Translation with Multilingual Pretrained Encoders ... Chen, Guanhua; Ma, Shuming; Chen, Yun. - : arXiv, 2021 BASE Show details 9 MT6: Multilingual Pretrained Text-to-Text Transformer with Translation Pairs ... Chi, Zewen; Dong, Li; Ma, Shuming. - : arXiv, 2021 BASE Show details 10 Multilingual Machine Translation Systems from Microsoft for WMT21 Shared Task ... Yang, Jian; Ma, Shuming; Huang, Haoyang. - : arXiv, 2021 BASE Show details 11 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 12 XLM-E: Cross-lingual Language Model Pre-training via ELECTRA ... Chi, Zewen; Huang, Shaohan; Dong, Li. - : arXiv, 2021 BASE Show details 13 How Does Distilled Data Complexity Impact the Quality and Confidence of Non-Autoregressive Machine Translation? ... Xu, Weijia; Ma, Shuming; Zhang, Dongdong. - : arXiv, 2021 BASE Show details 14 XLM-T: Scaling up Multilingual Machine Translation with Pretrained Cross-lingual Transformer Encoders ... Ma, Shuming; Yang, Jian; Huang, Haoyang. - : arXiv, 2020 BASE Show details 15 Deconvolution-Based Global Decoding for Neural Machine Translation ... Lin, Junyang; Sun, Xu; Ren, Xuancheng. - : arXiv, 2018 BASE Show details 16 A Semantic Relevance Based Neural Network for Text Summarization and Text Simplification ... Ma, Shuming; Sun, Xu. - : arXiv, 2017 Abstract: Text summarization and text simplification are two major ways to simplify the text for poor readers, including children, non-native speakers, and the functionally illiterate. Text summarization is to produce a brief summary of the main ideas of the text, while text simplification aims to reduce the linguistic complexity of the text and retain the original meaning. Recently, most approaches for text summarization and text simplification are based on the sequence-to-sequence model, which achieves much success in many text generation tasks. However, although the generated simplified texts are similar to source texts literally, they have low semantic relevance. In this work, our goal is to improve semantic relevance between source texts and simplified texts for text summarization and text simplification. We introduce a Semantic Relevance Based neural model to encourage high semantic similarity between texts and summaries. In our model, the source text is represented by a gated attention encoder, while the ... Keyword: Computation and Language cs.CL; FOS Computer and information sciences URL: https://arxiv.org/abs/1710.02318 https://dx.doi.org/10.48550/arxiv.1710.02318 BASE Hide details

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