Claim Verification

42 papers with code • 1 benchmarks • 2 datasets

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Most implemented papers

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

sheffieldnlp/fever-baselines NAACL 2018

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

Overview of CheckThat! 2020: Automatic Identification and Verification of Claims in Social Media

sshaar/clef2020-factchecking-task1 15 Jul 2020

The first four tasks compose the full pipeline of claim verification in social media: Task 1 on check-worthiness estimation, Task 2 on retrieving previously fact-checked claims, Task 3 on evidence retrieval, and Task 4 on claim verification.

MultiVerS: Improving scientific claim verification with weak supervision and full-document context

dwadden/longchecker Findings (NAACL) 2022

Our approach outperforms two competitive baselines on three scientific claim verification datasets, with particularly strong performance in zero / few-shot domain adaptation experiments.

Combining Fact Extraction and Verification with Neural Semantic Matching Networks

easonnie/combine-FEVER-NSMN 16 Nov 2018

The increasing concern with misinformation has stimulated research efforts on automatic fact checking.

BERT for Evidence Retrieval and Claim Verification

thunlp/KernelGAT 7 Oct 2019

Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction and verification challenge.

Fact or Fiction: Verifying Scientific Claims

allenai/scifact EMNLP 2020

We introduce scientific claim verification, a new task to select abstracts from the research literature containing evidence that SUPPORTS or REFUTES a given scientific claim, and to identify rationales justifying each decision.

Baleen: Robust Multi-Hop Reasoning at Scale via Condensed Retrieval

stanford-futuredata/ColBERT NeurIPS 2021

Multi-hop reasoning (i. e., reasoning across two or more documents) is a key ingredient for NLP models that leverage large corpora to exhibit broad knowledge.

AmbiFC: Fact-Checking Ambiguous Claims with Evidence

CambridgeNLIP/verification-real-world-info-needs 1 Apr 2021

Automated fact-checking systems verify claims against evidence to predict their veracity.

Attentive Convolution: Equipping CNNs with RNN-style Attention Mechanisms

kenkenling/NLP TACL 2018

We hypothesize that this is because the attention in CNNs has been mainly implemented as attentive pooling (i. e., it is applied to pooling) rather than as attentive convolution (i. e., it is integrated into convolution).

TwoWingOS: A Two-Wing Optimization Strategy for Evidential Claim Verification

yinwenpeng/FEVER EMNLP 2018

We develop TwoWingOS (two-wing optimization strategy), a system that, while identifying appropriate evidence for a claim, also determines whether or not the claim is supported by the evidence.