4 citations · 12 across the 7 of their papers we have counts for
7 papers
An Understanding-Oriented Robust Machine Reading Comprehension Model
Feiliang Ren, Yongkang Liu, Bochao Li +5
Although existing machine reading comprehension models are making rapid progress on many datasets, they are far from robust. In this paper, we propose an understanding-oriented mac…
Deep Understanding based Multi-Document Machine Reading Comprehension
Feiliang Ren, Yongkang Liu, Bochao Li +7
Most existing multi-document machine reading comprehension models mainly focus on understanding the interactions between the input question and documents, but ignore following two…
A Simple but Effective Bidirectional Framework for Relational Triple Extraction
Feiliang Ren, Longhui Zhang, Xiaofeng Zhao +3
Tagging based relational triple extraction methods are attracting growing research attention recently. However, most of these methods take a unidirectional extraction framework tha…
A Three-Stage Learning Framework for Low-Resource Knowledge-Grounded Dialogue Generation
Shilei Liu, Xiaofeng Zhao, Bochao Li +3
Neural conversation models have shown great potentials towards generating fluent and informative responses by introducing external background knowledge. Nevertheless, it is laborio…
Knowledge-Grounded Dialogue with Reward-Driven Knowledge Selection
Shilei Liu, Xiaofeng Zhao, Bochao Li +1
Knowledge-grounded dialogue is a task of generating a fluent and informative response based on both conversation context and a collection of external knowledge, in which knowledge…
A Graph Reasoning Network for Multi-turn Response Selection via Customized Pre-training
Yongkang Liu, Shi Feng, Daling Wang +3
We investigate response selection for multi-turn conversation in retrieval-based chatbots. Existing studies pay more attention to the matching between utterances and responses by c…
Knowledge Graph Embedding with Atrous Convolution and Residual Learning
Feiliang Ren, Juchen Li, Huihui Zhang +4
Knowledge graph embedding is an important task and it will benefit lots of downstream applications. Currently, deep neural networks based methods achieve state-of-the-art performan…