Evidence Selection as a Token-Level Prediction Task

By Dominik Stammbach. 2021

In Automated Claim Verification, we retrieve evidence from a knowledge base to determine the veracity of a claim. Intuitively, the retrieval of the correct evidence plays a crucial role in this process. Often, evidence selection is tackled as a pairwise sentence classification task, i.e., we train a model to predict for each sentence individually whether it is evidence for a claim. In this work, we fine-tune document level transformers to extract all evidence from a Wikipedia document at once. We show that this approach performs better than a comparable model classifying sentences individually on all relevant evidence selection metrics in FEVER. Our complete pipeline building on this evidence selection procedure produces a new state-of-the-art result on FEVER, a popular claim verification benchmark.

Subfields: Information Retrieval & Search, Natural Language Processing

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ProoFVer: Natural Logic Theorem Proving for Fact Verification (2021, background): “DominikS (Stammbach, 2021) focuses primarily on sentence-level evidence retrieval, scoring individual tokens from a given Wikipedia document, and then selecting the highest scoring sentences by averaging token scores.”