BERT for Evidence Retrieval and Claim Verification

Understand

Motivated by the promising performance of pre-trained language models, we investigate BERT in an evidence retrieval and claim verification pipeline for the FEVER fact extraction and verification challenge.

• To this end, we propose to use two BERT models, one for retrieving potential evidence sentences supporting or rejecting claims, and another for verifying claims based on the predicted evidence sets.
• To train the BERT retrieval system, we use pointwise and pairwise loss functions, and examine the effect of hard negative mining.
• A second BERT model is trained to classify the samples as supported, refuted, and not enough information.