4 citations · 10 across the 9 of their papers we have counts for
9 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 Novel Global Feature-Oriented Relational Triple Extraction Model based on Table Filling
Feiliang Ren, Longhui Zhang, Shujuan Yin +4
Table filling based relational triple extraction methods are attracting growing research interests due to their promising performance and their abilities on extracting triples from…
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 Conditional Cascade Model for Relational Triple Extraction
Feiliang Ren, Longhui Zhang, Shujuan Yin +3
Tagging based methods are one of the mainstream methods in relational triple extraction. However, most of them suffer from the class imbalance issue greatly. Here we propose a nove…
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…
LMVE at SemEval-2020 Task 4: Commonsense Validation and Explanation using Pretraining Language Model
Shilei Liu, Yu Guo, Bochao Li +1
This paper describes our submission to subtask a and b of SemEval-2020 Task 4. For subtask a, we use a ALBERT based model with improved input form to pick out the common sense stat…