Improving Large-Scale Fact-Checking using Decomposable Attention Models and Lexical Tagging (D18-1)
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| Challenge: | Existing pipelines for fact-checking of textual sources are limited . fact- checking of text sources requires a large knowledge base to extract relevant information . |
| Approach: | They propose a neural ranker that dynamically selects sentences to improve evidence retrieval . they incorporate lexical tagging methods into the pipeline framework to simplify the tasks . |
| Outcome: | The proposed model outperforms the existing TF-IDF method on a large-scale fact extraction and verification dataset with speedup. |
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