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 implementation • 6 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

Uncovering Main Causalities for Long-tailed Information Extraction

1 code implementation • EMNLP 2021 • Guoshun Nan, Jiaqi Zeng, Rui Qiao, Zhijiang Guo, Wei Lu

Information Extraction (IE) aims to extract structural information from unstructured texts.

Causal Inference Selection bias

A Survey on Automated Fact-Checking

1 code implementation • 26 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 implementation • 10 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

Lightweight, Dynamic Graph Convolutional Networks for AMR-to-Text Generation

1 code implementation • EMNLP 2020 • Yan Zhang, Zhijiang Guo, Zhiyang Teng, Wei Lu, Shay B. Cohen, Zuozhu Liu, Lidong Bing

With the help of these strategies, we are able to train a model with fewer parameters while maintaining the model capacity.

AMR-to-Text Generation Text Generation

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