most citedFEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

52 citations · 52 across the 1 of their papers we have counts for

collaborators

6 papers

cs.CL2021

Evidence-based Factual Error Correction

James Thorne, Andreas Vlachos

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied…

cs.CL2021

Database Reasoning Over Text

James Thorne, Majid Yazdani, Marzieh Saeidi +3

Neural models have shown impressive performance gains in answering queries from natural language text. However, existing works are unable to support database queries, such as "List…

cs.CL202152 cited

FEVEROUS: Fact Extraction and VERification Over Unstructured and Structured information

Rami Aly, Zhijiang Guo, Michael Schlichtkrull +5

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…

cs.CL2020

Evidence-based Factual Error Correction

James Thorne, Andreas Vlachos

This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence. This extends the well-studied…

cs.CL2020

Neural Databases

James Thorne, Majid Yazdani, Marzieh Saeidi +3

In recent years, neural networks have shown impressive performance gains on long-standing AI problems, and in particular, answering queries from natural language text. These advanc…

cs.CL2020

Elastic weight consolidation for better bias inoculation

James Thorne, Andreas Vlachos

The biases present in training datasets have been shown to affect models for sentence pair classification tasks such as natural language inference (NLI) and fact verification. Whil…