activity
20192022
most citedLearning to Generate Questions by Learning What not to Generate

105 citations · 161 across the 5 of their papers we have counts for

collaborators

6 papers

cs.IR2022

Modeling User Repeat Consumption Behavior for Online Novel Recommendation

Yuncong Li, Cunxiang Yin, Yancheng He +4

Given a user's historical interaction sequence, online novel recommendation suggests the next novel the user may be interested in. Online novel recommendation is important but unde…

cs.CL20211 cited

Aspect-Sentiment-Multiple-Opinion Triplet Extraction

Fang Wang, Yuncong Li, Sheng-hua Zhong +2

Aspect Sentiment Triplet Extraction (ASTE) aims to extract aspect term (aspect), sentiment and opinion term (opinion) triplets from sentences and can tell a complete story, i.e., t…

cs.CL202046 cited

Asking Questions the Human Way: Scalable Question-Answer Generation from Text Corpus

Bang Liu, Haojie Wei, Di Niu +2

The ability to ask questions is important in both human and machine intelligence. Learning to ask questions helps knowledge acquisition, improves question-answering and machine rea…

cs.CL2019

Recursive Graphical Neural Networks for Text Classification

Wei Li, Shuheng Li, Shuming Ma +3

The complicated syntax structure of natural language is hard to be explicitly modeled by sequence-based models. Graph is a natural structure to describe the complicated relation be…

cs.CL20199 cited

Coherent Comment Generation for Chinese Articles with a Graph-to-Sequence Model

Wei Li, Jingjing Xu, Yancheng He +3

Automatic article commenting is helpful in encouraging user engagement and interaction on online news platforms. However, the news documents are usually too long for traditional en…

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…