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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; Bing, Lidong; Lam, Wai. - : arXiv, 2017 Abstract: Recently, some E-commerce sites launch a new interaction box called Tips on their mobile apps. Users can express their experience and feelings or provide suggestions using short texts typically several words or one sentence. In essence, writing some tips and giving a numerical rating are two facets of a user's product assessment action, expressing the user experience and feelings. Jointly modeling these two facets is helpful for designing a better recommendation system. While some existing models integrate text information such as item specifications or user reviews into user and item latent factors for improving the rating prediction, no existing works consider tips for improving recommendation quality. We propose a deep learning based framework named NRT which can simultaneously predict precise ratings and generate abstractive tips with good linguistic quality simulating user experience and feelings. For abstractive tips generation, gated recurrent neural networks are employed to "translate" user and item ... : SIGIR 2017 ... Keyword: Artificial Intelligence cs.AI; Computation and Language cs.CL; FOS Computer and information sciences; Information Retrieval cs.IR URL: https://arxiv.org/abs/1708.00154 https://dx.doi.org/10.48550/arxiv.1708.00154 BASE Hide details 10 Reader-Aware Multi-Document Summarization via Sparse Coding ... Li, Piji; Bing, Lidong; Lam, Wai. - : arXiv, 2015 BASE Show 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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