Fact Verification

24 papers with code • 2 benchmarks • 7 datasets

Fact verification, also called "fact checking", is a process of verifying facts in natural text against a database of facts.

Greatest papers with code

KILT: a Benchmark for Knowledge Intensive Language Tasks

facebookresearch/KILT NAACL 2021

We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.

Entity Linking Fact Checking +4

Program Enhanced Fact Verification with Verbalization and Graph Attention Network

wenhuchen/Table-Fact-Checking EMNLP 2020

Built on that, we construct the graph attention verification networks, which are designed to fuse different sources of evidences from verbalized program execution, program structures, and the original statements and tables, to make the final verification decision.

Fact Verification

TabFact: A Large-scale Dataset for Table-based Fact Verification

wenhuchen/Table-Fact-Checking ICLR 2020

To this end, we construct a large-scale dataset called TabFact with 16k Wikipedia tables as the evidence for 118k human-annotated natural language statements, which are labeled as either ENTAILED or REFUTED.

Fact Checking Fact Verification +3

Fine-grained Fact Verification with Kernel Graph Attention Network

thunlp/KernelGAT ACL 2020

Fact Verification requires fine-grained natural language inference capability that finds subtle clues to identify the syntactical and semantically correct but not well-supported claims.

Fact Verification Natural Language Inference

GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification

thunlp/KernelGAT ACL 2019

Fact verification (FV) is a challenging task which requires to retrieve relevant evidence from plain text and use the evidence to verify given claims.

Fact Verification

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.

Fact Checking Fact Verification +2

Revealing the Importance of Semantic Retrieval for Machine Reading at Scale

easonnie/semanticRetrievalMRS IJCNLP 2019

In this work, we give general guidelines on system design for MRS by proposing a simple yet effective pipeline system with special consideration on hierarchical semantic retrieval at both paragraph and sentence level, and their potential effects on the downstream task.

Fact Verification Information Retrieval +3

Towards Debiasing Fact Verification Models

TalSchuster/FeverSymmetric IJCNLP 2019

Fact verification requires validating a claim in the context of evidence.

Fact Verification

Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News

nguyenvo09/EMNLP2020 EMNLP 2020

The search can directly warn fake news posters and online users (e. g. the posters' followers) about misinformation, discourage them from spreading fake news, and scale up verified content on social media.

Ad-Hoc Information Retrieval Fact Checking +5