UCL Machine Reading Group: Four Factor Framework For Fact Finding (HexaF)
In this paper we describe our 2nd place FEVER shared-task system that achieved a FEVER score of 62.52{\%} on the provisional test set (without additional human evaluation), and 65.41{\%} on the development set. Our system is a four stage model consisting of document retrieval, sentence retrieval, natural language inference and aggregation. Retrieval is performed leveraging task-specific features, and then a natural language inference model takes each of the retrieved sentences paired with the claimed fact. The resulting predictions are aggregated across retrieved sentences with a Multi-Layer Perceptron, and re-ranked corresponding to the final prediction.
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