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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 – 11 of 11 1 Enhancing Cross-lingual Prompting with Mask Token Augmentation ... Zhou, Meng; Li, Xin; Jiang, Yue. - : arXiv, 2022 BASE Show details 2 Towards Multi-Sense Cross-Lingual Alignment of Contextual Embeddings ... Liu, Linlin; Nguyen, Thien Hai; Joty, Shafiq. - : arXiv, 2021 BASE Show details 3 Knowledge Based Multilingual Language Model ... Liu, Linlin; Li, Xin; He, Ruidan. - : arXiv, 2021 BASE Show details 4 MELM: Data Augmentation with Masked Entity Language Modeling for Low-Resource NER ... Zhou, Ran; Li, Xin; He, Ruidan. - : arXiv, 2021 BASE Show details 5 Multilingual AMR Parsing with Noisy Knowledge Distillation ... Cai, Deng; Li, Xin; Ho, Jackie Chun-Sing. - : arXiv, 2021 BASE Show details 6 GlobalWoZ: Globalizing MultiWoZ to Develop Multilingual Task-Oriented Dialogue Systems ... Ding, Bosheng; Hu, Junjie; Bing, Lidong. - : arXiv, 2021 BASE Show details 7 Unsupervised Cross-lingual Adaptation for Sequence Tagging and Beyond ... Li, Xin; Bing, Lidong; Zhang, Wenxuan. - : arXiv, 2020 BASE Show details 8 Transferable End-to-End Aspect-based Sentiment Analysis with Selective Adversarial Learning ... Li, Zheng; Li, Xin; Wei, Ying. - : arXiv, 2019 BASE Show details 9 Neural Rating Regression with Abstractive Tips Generation for Recommendation ... Li, Piji; Wang, Zihao; Ren, Zhaochun. - : arXiv, 2017 BASE Show details 10 Reader-Aware Multi-Document Summarization via Sparse Coding ... Li, Piji; Bing, Lidong; Lam, Wai; Li, Hang; Liao, Yi. - : arXiv, 2015 Abstract: We propose a new MDS paradigm called reader-aware multi-document summarization (RA-MDS). Specifically, a set of reader comments associated with the news reports are also collected. The generated summaries from the reports for the event should be salient according to not only the reports but also the reader comments. To tackle this RA-MDS problem, we propose a sparse-coding-based method that is able to calculate the salience of the text units by jointly considering news reports and reader comments. Another reader-aware characteristic of our framework is to improve linguistic quality via entity rewriting. The rewriting consideration is jointly assessed together with other summarization requirements under a unified optimization model. To support the generation of compressive summaries via optimization, we explore a finer syntactic unit, namely, noun/verb phrase. In this work, we also generate a data set for conducting RA-MDS. Extensive experiments on this data set and some classical data sets demonstrate the ... : 7 pages, 2 figures, accepted as a full paper at IJCAI 2015 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences URL: https://dx.doi.org/10.48550/arxiv.1504.07324 https://arxiv.org/abs/1504.07324 BASE Hide details 11 Abstractive Multi-Document Summarization via Phrase Selection and Merging ... Bing, Lidong; Li, Piji; Liao, Yi. - : arXiv, 2015 BASE Show details

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