Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging

By Nayeon Lee, Chien-Sheng Wu, Pascale Fung. 2018

25 citations recorded by the source corpus.

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 (x65) 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.

Subfields: Artificial Intelligence, Natural Language Processing

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Citations in the corpus

Survey of Hallucination in Natural Language Generation (2022, background): “For the veriication subtask, veriication models perform relatively well if given correct evidence [74].”
Generating Fact Checking Briefs (2020, background): “The FEVER Challenge (Thorne et al., 2018) proposed providing provenance for a decision along with classification, and various approaches developed combine information retrieval with stance detection or question answering (Li et al., 2018; Lee et al., 2018).”
Language Models as Fact Checkers? (2020, background): “Traditional pipeline Traditional fact-checking pipelines (Malon, 2018; Hanselowski et al., 2018; Yoneda et al., 2018; Lee et al., 2018; Nie et al., 2019) access knowledge within an external knowledge base (i.”