16 citations · 32 across the 6 of their papers we have counts for
6 papers
Teach model to answer questions after comprehending the document
Ruiqing Sun, Ping Jian
Multi-choice Machine Reading Comprehension (MRC) is a challenging extension of Natural Language Processing (NLP) that requires the ability to comprehend the semantics and logical r…
MS-Ranker: Accumulating Evidence from Potentially Correct Candidates for Answer Selection
Yingxue Zhang, Fandong Meng, Peng Li +2
As conventional answer selection (AS) methods generally match the question with each candidate answer independently, they suffer from the lack of matching information between the q…
Tag Recommendation by Word-Level Tag Sequence Modeling
Xuewen Shi, Heyan Huang, Shuyang Zhao +2
In this paper, we transform tag recommendation into a word-based text generation problem and introduce a sequence-to-sequence model. The model inherits the advantages of LSTM-based…
Neural Chinese Word Segmentation as Sequence to Sequence Translation
Xuewen Shi, Heyan Huang, Ping Jian +3
Recently, Chinese word segmentation (CWS) methods using neural networks have made impressive progress. Most of them regard the CWS as a sequence labeling problem which construct mo…
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Yingxue Zhang, Ping Jian, Fandong Meng +3
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives comm…
Induction Networks for Few-Shot Text Classification
Ruiying Geng, Binhua Li, Yongbin Li +3
Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to s…