Search Results for author: Zhijiang Guo

Found 10 papers, 8 papers with code

The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task

no code implementations EMNLP (FEVER) 2021 Rami Aly, Zhijiang Guo, Michael Sejr Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal

The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence retrieved from Wikipedia (or NotEnoughInfo if the claim cannot be verified).

CHEF: A Pilot Chinese Dataset for Evidence-Based Fact-Checking

1 code implementation6 Jun 2022 Xuming Hu, Zhijiang Guo, Guanyu Wu, Aiwei Liu, Lijie Wen, Philip S. Yu

The explosion of misinformation spreading in the media ecosystem urges for automated fact-checking.

Fact Checking Misinformation

A Survey on Automated Fact-Checking

1 code implementation26 Aug 2021 Zhijiang Guo, Michael Schlichtkrull, Andreas Vlachos

Fact-checking has become increasingly important due to the speed with which both information and misinformation can spread in the modern media ecosystem.

Fact Checking Misinformation +1

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

1 code implementation10 Jun 2021 Rami Aly, Zhijiang Guo, Michael Schlichtkrull, James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Oana Cocarascu, Arpit Mittal

Fact verification has attracted a lot of attention in the machine learning and natural language processing communities, as it is one of the key methods for detecting misinformation.

Fact Verification Misinformation +1

Reasoning with Latent Structure Refinement for Document-Level Relation Extraction

1 code implementation ACL 2020 Guoshun Nan, Zhijiang Guo, Ivan Sekulić, Wei Lu

Document-level relation extraction requires integrating information within and across multiple sentences of a document and capturing complex interactions between inter-sentence entities.

Relational Reasoning Relation Extraction

Densely Connected Graph Convolutional Networks for Graph-to-Sequence Learning

1 code implementation TACL 2019 Zhijiang Guo, Yan Zhang, Zhiyang Teng, Wei Lu

We focus on graph-to-sequence learning, which can be framed as transducing graph structures to sequences for text generation.

Graph-to-Sequence Machine Translation +2

Attention Guided Graph Convolutional Networks for Relation Extraction

2 code implementations ACL 2019 Zhijiang Guo, Yan Zhang, Wei Lu

Dependency trees convey rich structural information that is proven useful for extracting relations among entities in text.

Relation Extraction

Better Transition-Based AMR Parsing with a Refined Search Space

no code implementations EMNLP 2018 Zhijiang Guo, Wei Lu

This paper introduces a simple yet effective transition-based system for Abstract Meaning Representation (AMR) parsing.

AMR Parsing Named Entity Recognition +1

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