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20182022
most citedLearning to Generate Questions by Learning What not to Generate

105 citations · 179 across the 5 of their papers we have counts for

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Showing cs.CLShow all

6 papers · 1 filter

cs.CL2022

Mulco: Recognizing Chinese Nested Named Entities Through Multiple Scopes

Jiuding Yang, Jinwen Luo, Weidong Guo +3

Nested Named Entity Recognition (NNER) has been a long-term challenge to researchers as an important sub-area of Named Entity Recognition. NNER is where one entity may be part of a…

cs.CL20209 cited

GIANT: Scalable Creation of a Web-scale Ontology

Bang Liu, Weidong Guo, Di Niu +4

Understanding what online users may pay attention to is key to content recommendation and search services. These services will benefit from a highly structured and web-scale ontolo…

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.CL2018

Matching Natural Language Sentences with Hierarchical Sentence Factorization

Bang Liu, Ting Zhang, Fred X. Han +3

Semantic matching of natural language sentences or identifying the relationship between two sentences is a core research problem underlying many natural language tasks. Depending o…

cs.CL2018

Matching Article Pairs with Graphical Decomposition and Convolutions

Bang Liu, Di Niu, Haojie Wei +4

Identifying the relationship between two articles, e.g., whether two articles published from different sources describe the same breaking news, is critical to many document underst…