Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases. In this paper, we extend an existing pipeline approach to better tackle this problem. We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80{\%}, with (x65) speedup compared to a TF-IDF method. Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable. As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.
Code (0)
등록된 구현이 없습니다.
Tasks
Fact CheckingQuestion AnsweringRetrievalStance DetectionSimilar Papers 제목 키워드 기반
Automated Fact-Checking of Claims from Wikipedia
Automated fact checking is becoming increasingly vital as both truthful and fallacious information accumulate online. Research on fact checking has benefited from large-scale datasets such as FEVER and SNLI. However, suc…
ArticlesFact CheckingUni-DUE Student Team: Tackling fact checking through decomposable attention neural network
In this paper we present our system for the FEVER Challenge. The task of this challenge is to verify claims by extracting information from Wikipedia. Our system has two parts. In the first part it performs a search for c…
Fact CheckingA Multi-Level Attention Model for Evidence-Based Fact Checking
Evidence-based fact checking aims to verify the truthfulness of a claim against evidence extracted from textual sources. Learning a representation that effectively captures relations between a claim and evidence can be c…
Fact CheckingSentenceBeyond Translation: LLM-Based Data Generation for Multilingual Fact-Checking
Robust automatic fact-checking systems have the potential to combat online misinformation at scale. However, most existing research primarily focuses on English. In this paper, we introduce MultiSynFact, the first large-…
Dataset GenerationFact CheckingMisinformationTranslationCrowdsourced Fact-Checking at Twitter: How Does the Crowd Compare With Experts?
Fact-checking is one of the effective solutions in fighting online misinformation. However, traditional fact-checking is a process requiring scarce expert human resources, and thus does not scale well on social media bec…
Fact CheckingMisinformation