Fact Verification
65 papers with code • 3 benchmarks • 14 datasets
Fact verification, also called "fact checking", is a process of verifying facts in natural text against a database of facts.
Benchmarks
These leaderboards are used to track progress in Fact Verification
| Trend | Dataset | Best Model | Paper | Code | Compare |
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FEVER
KILT
VitaminC
FEVEROUS
Snopes
CREAK
X-Fact
FaVIQ
Most implemented papers
Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks
huggingface/transformers
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NeurIPS 2020
Large pre-trained language models have been shown to store factual knowledge in their parameters, and achieve state-of-the-art results when fine-tuned on downstream NLP tasks.
Towards Debiasing Fact Verification Models
TalSchuster/FeverSymmetric
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IJCNLP 2019
Fact verification requires validating a claim in the context of evidence.
KILT: a Benchmark for Knowledge Intensive Language Tasks
We test both task-specific and general baselines, evaluating downstream performance in addition to the ability of the models to provide provenance.
Evidence-based Factual Error Correction
j6mes/2021-acl-factual-error-correction
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This paper introduces the task of factual error correction: performing edits to a claim so that the generated rewrite is better supported by evidence.
Combining Fact Extraction and Verification with Neural Semantic Matching Networks
easonnie/combine-FEVER-NSMN
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The increasing concern with misinformation has stimulated research efforts on automatic fact checking.
GEAR: Graph-based Evidence Aggregating and Reasoning for Fact Verification
thunlp/GEAR
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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.
End-to-End Bias Mitigation by Modelling Biases in Corpora
rabeehk/robust-nli
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ACL 2020
We experiment on large-scale natural language inference and fact verification benchmarks, evaluating on out-of-domain datasets that are specifically designed to assess the robustness of models against known biases in the training data.
Revealing the Importance of Semantic Retrieval for Machine Reading at Scale
easonnie/semanticRetrievalMRS
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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.
Where Are the Facts? Searching for Fact-checked Information to Alleviate the Spread of Fake News
nguyenvo09/EMNLP2020
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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.
Multilingual Evidence Retrieval and Fact Verification to Combat Global Disinformation: The Power of Polyglotism
D-Roberts/multilingual_nli_ECIR2021
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This article investigates multilingual evidence retrieval and fact verification as a step to combat global disinformation, a first effort of this kind, to the best of our knowledge.
