Search Results for author: James Thorne

Found 18 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).

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

Evidence-based Factual Error Correction

1 code implementation ACL 2021 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.

Fact Checking Fact Verification

Database Reasoning Over Text

1 code implementation ACL 2021 James Thorne, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, Alon Halevy

Neural models have shown impressive performance gains in answering queries from natural language text.

Evidence-based Factual Error Correction

2 code implementations31 Dec 2020 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.

Fact Checking Fact Verification +1

Neural Databases

no code implementations14 Oct 2020 James Thorne, Majid Yazdani, Marzieh Saeidi, Fabrizio Silvestri, Sebastian Riedel, Alon Halevy

We describe NeuralDB, a database system with no pre-defined schema, in which updates and queries are given in natural language.

Elastic weight consolidation for better bias inoculation

1 code implementation EACL 2021 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.

Fact Verification General Classification +1

Generating Token-Level Explanations for Natural Language Inference

no code implementations NAACL 2019 James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

In this paper, we show that it is possible to generate token-level explanations for NLI without the need for training data explicitly annotated for this purpose.

Multiple Instance Learning Natural Language Inference

Adversarial attacks against Fact Extraction and VERification

no code implementations13 Mar 2019 James Thorne, Andreas Vlachos

This paper describes a baseline for the second iteration of the Fact Extraction and VERification shared task (FEVER2. 0) which explores the resilience of systems through adversarial evaluation.

Fact Checking Information Retrieval +1

Automated Fact Checking: Task formulations, methods and future directions

no code implementations COLING 2018 James Thorne, Andreas Vlachos

The recently increased focus on misinformation has stimulated research in fact checking, the task of assessing the truthfulness of a claim.

Fact Checking Misinformation

FEVER: a large-scale dataset for Fact Extraction and VERification

5 code implementations NAACL 2018 James Thorne, Andreas Vlachos, Christos Christodoulopoulos, Arpit Mittal

Thus we believe that FEVER is a challenging testbed that will help stimulate progress on claim verification against textual sources.

An Extensible Framework for Verification of Numerical Claims

no code implementations EACL 2017 James Thorne, Andreas Vlachos

In this paper we present our automated fact checking system demonstration which we developed in order to participate in the Fast and Furious Fact Check challenge.

Fact Checking Rumour Detection +1

Cannot find the paper you are looking for? You can Submit a new open access paper.