R
Rui Wang
Researcher at Alibaba Group
Publications - 78
Citations - 1506
Rui Wang is an academic researcher from Alibaba Group. The author has contributed to research in topics: Parsing & Textual entailment. The author has an hindex of 19, co-authored 72 publications receiving 995 citations. Previous affiliations of Rui Wang include Saarland University & German Research Centre for Artificial Intelligence.
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Relational Graph Attention Network for Aspect-based Sentiment Analysis
TL;DR: This paper defines a unified aspect-oriented dependency tree structure rooted at a target aspect by reshaping and pruning an ordinary dependency parse tree and proposes a relational graph attention network (R-GAT) to encode the new tree structure for sentiment prediction.
Proceedings ArticleDOI
Relational Graph Attention Network for Aspect-based Sentiment Analysis
TL;DR: This article proposed a relational graph attention network (R-GATN) to encode the new tree structure for sentiment prediction. But, due to the complexity of language and the existence of multiple aspects in a single sentence, these models often confuse the connections.
Proceedings ArticleDOI
Recognizing Textual Entailment Using Sentence Similarity based on Dependency Tree Skeletons
Rui Wang,Günter Neumann +1 more
TL;DR: A novel approach to RTE that exploits a structure-oriented sentence representation followed by a similarity function that makes use of a limited size of training data without any external knowledge bases or handcrafted inference rules is presented.
Journal ArticleDOI
Boundary Enhanced Neural Span Classification for Nested Named Entity Recognition.
TL;DR: This work proposes a boundary enhanced neural span classification model that has the ability to generate high-quality candidate spans and greatly reduces the time complexity during inference, and incorporates an additional boundary detection task to predict those words that are boundaries of entities.
Proceedings ArticleDOI
BiSET: Bi-directional Selective Encoding with Template for Abstractive Summarization
Kai Wang,Xiaojun Quan,Rui Wang +2 more
TL;DR: A novel Bi-directional Selective Encoding with Template (BiSET) model, which leverages template discovered from training data to softly select key information from each source article to guide its summarization process, is proposed.