most citedA Simple but Effective Bidirectional Framework for Relational Triple Extraction

3 citations · 3 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL20223 cited

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…

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021

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…

cs.CL2021

An Effective System for Multi-format Information Extraction

Yaduo Liu, Longhui Zhang, Shujuan Yin +2

The multi-format information extraction task in the 2021 Language and Intelligence Challenge is designed to comprehensively evaluate information extraction from different dimension…