Skip to content

Neural Networks for NLP Project: Fact Extraction and Verification (FEVER)

Notifications You must be signed in to change notification settings

aditya5558/BERT-FEVER-Task

Folders and files

NameName
Last commit message
Last commit date

Latest commit

 

History

57 Commits
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 
 

Repository files navigation

Neural Networks for NLP Project: Fact Extraction and Verification

Authors

Aditya Anantharaman (AndrewID: adityaan)
Derik Clive Robert (AndrewID: dclive)
Abhinav Khattar (AndrewID: akhattar)

About the Project

The increasing cases of manipulation of facts, misleading information and unverified claims make automatic fact verification an important task. The Fact Extraction and Verification (FEVER) Shared Task provides a large-scale dataset of claims and Wikipedia documents as evidence to verify these claims. In this work, we tackle this task by building an end-to-end neural model which given a claim, extracts relevant documents, then matches informative sentences in these documents with the claim to verify the claim. We build upon the BERT based approach proposed by Soleimani et al. and strengthen the claim verification module using Multi-Task Deep Neural Networks (MT-DNN) and Stochastic Answer Networks (SAN) in addition to multi-hop evidence reasoning. We show that our approach is able to outperform even the BERT-Large model proposed by Soleimani et al. using the BERT-Base architecture in terms of label accuracy which showcases the effectiveness of our improved claim verification model.

Pipeline

Pipeline

General

The repo contains .py files required to run the code.
To replicate Soleimani et al. results, run code in the following order:

  1. doc_retrieval.py: retrieves docs using the MediaWiki API
  2. sentence_retrieval.py: retrieves top 5 sentences for every claim
  3. claim_verification.py: classifies the top 5 sentences for every claim

Files for other specific implementations inside respective folders.

Dev Set Results

Model Fever Score (%) Label Accuracy (%)
BERT-Pointwise (Soleimani et al.) 71.38 73.5
BERT-Large (Pointwise + HNM) (Soleimani et al.) 72.42 74.59
BERT-Pointwise re-implementation 68.11 71.13
XLNet 69.13 72.96
RoBERTa 69.89 73.11
RoBERTa Multi-hop 70.55 74.95
MT-DNN Multi-hop 70.02 74.82
MT-DNN + SAN Multi-hop 70.52 75.17

Reference

Repo inspired by:
BERT for Evidence Retrieval and Claim Verification by Soleimani et al.

About

Neural Networks for NLP Project: Fact Extraction and Verification (FEVER)

Resources

Stars

Watchers

Forks

Releases

No releases published

Packages

No packages published