Links to conference/journal publications in automated fact-checking (resources for the TACL22/EMNLP23 paper).
This repo contains relevant resources from our survey paper A Survey on Automated Fact-Checking in TACL 2022 and the follow up multimodal survey paper Multimodal Automated Fact-Checking: A Survey. In this survey, we present a comprehensive and up-to-date survey of automated fact-checking (AFC), unifying various components and definitions developed in previous research into a common framework. As automated fact-checking research is evolving, we will provide timely updates on the survey and this repo.
Figure below shows a NLP framework for automated fact-checking (AFC) with text consisting of three stages:
Evidence retrieval and claim verification are sometimes tackled as a single task referred to asfactual verification, while claim detection is often tackled separately. Claim verificationcan be decomposed into two parts that can be tackled separately or jointly: verdict prediction, where claims are assigned truthfulness labels, and justification production, where explanations for verdicts must be produced.
In the follow up multimodal survey, we extends the first stage with a claim extraction step, and generalises the third stage to cover tasks that fall under multimodal AFC: