Team UMBC-FEVER : Claim verification using Semantic Lexical Resources
We describe our system used in the 2018 FEVER shared task. The system employed a frame-based information retrieval approach to select Wikipedia sentences providing evidence and used a two-layer multilayer perceptron to classify a claim as correct or not. Our submission achieved a score of 0.3966 on the Evidence F1 metric with accuracy of 44.79{\%}, and FEVER score of 0.2628 F1 points.
Code (0)
등록된 구현이 없습니다.
Tasks
Claim VerificationInformation RetrievalRetrievalSimilar Papers 제목 키워드 기반
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) Shared Task
The Fact Extraction and VERification Over Unstructured and Structured information (FEVEROUS) shared task, asks participating systems to determine whether human-authored claims are Supported or Refuted based on evidence r…
RetrievalThe Fact Extraction and VERification (FEVER) Shared Task
We present the results of the first Fact Extraction and VERification (FEVER) Shared Task. The task challenged participants to classify whether human-written factoid claims could be Supported or Refuted using evidence ret…
Team Papelo: Transformer Networks at FEVER
We develop a system for the FEVER fact extraction and verification challenge that uses a high precision entailment classifier based on transformer networks pretrained with language modeling, to classify a broad set of po…
ArticlesLanguage ModelingLanguage ModellingTeam DOMLIN: Exploiting Evidence Enhancement for the FEVER Shared Task
This paper contains our system description for the second Fact Extraction and VERification (FEVER) challenge. We propose a two-staged sentence selection strategy to account for examples in the dataset where evidence is n…
RetrievalSentenceRobust Document Retrieval and Individual Evidence Modeling for Fact Extraction and Verification.
This paper presents the ColumbiaNLP submission for the FEVER Workshop Shared Task. Our system is an end-to-end pipeline that extracts factual evidence from Wikipedia and infers a decision about the truthfulness of the cl…
Natural Language InferenceRetrievalSentence