activity
20182021
most citedLearning to Generate Questions by Learning What not to Generate

105 citations · 183 across the 5 of their papers we have counts for

collaborators

8 papers

cs.AI20211 cited

KGSynNet: A Novel Entity Synonyms Discovery Framework with Knowledge Graph

Yiying Yang, Xi Yin, Haiqin Yang +5

Entity synonyms discovery is crucial for entity-leveraging applications. However, existing studies suffer from several critical issues: (1) the input mentions may be out-of-vocabul…

cs.SI201912 cited

Fine-grained Event Categorization with Heterogeneous Graph Convolutional Networks

Hao Peng, Jianxin Li, Qiran Gong +4

Events are happening in real-world and real-time, which can be planned and organized occasions involving multiple people and objects. Social media platforms publish a lot of text m…

cs.IR201926 cited

A User-Centered Concept Mining System for Query and Document Understanding at Tencent

Bang Liu, Weidong Guo, Di Niu +5

Concepts embody the knowledge of the world and facilitate the cognitive processes of human beings. Mining concepts from web documents and constructing the corresponding taxonomy ar…

cs.CL201939 cited

Multiresolution Graph Attention Networks for Relevance Matching

Ting Zhang, Bang Liu, Di Niu +2

A large number of deep learning models have been proposed for the text matching problem, which is at the core of various typical natural language processing (NLP) tasks. However, e…

cs.CL2019105 cited

Learning to Generate Questions by Learning What not to Generate

Bang Liu, Mingjun Zhao, Di Niu +4

Automatic question generation is an important technique that can improve the training of question answering, help chatbots to start or continue a conversation with humans, and prov…

cs.IR2018

Growing Story Forest Online from Massive Breaking News

Bang Liu, Di Niu, Kunfeng Lai +2

We describe our experience of implementing a news content organization system at Tencent that discovers events from vast streams of breaking news and evolves news story structures…