Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging
Nayeon Lee, Chien-Sheng Wu, Pascale Fung · 2018
Fact-checking of textual sources needs to effectively extract relevant information from large knowledge bases.In this paper, we extend an existing pipeline approach to better tackle this problem.We propose a neural ranker using a decomposable attention model that dynamically selects sentences to achieve promising improvement in evidence retrieval F1 by 38.80%, with (×65) speedup compared to a TF-IDF method.Moreover, we incorporate lexical tagging methods into our pipeline framework to simplify the tasks and render the model more generalizable.As a result, our framework achieves promising performance on a large-scale fact extraction and verification dataset with speedup.