AttentiveChecker: A Bi-Directional Attention Flow Mechanism for Fact Verification

Santosh Tokala, Vishal G, Avirup Saha, Niloy Ganguly


Abstract
The recently released FEVER dataset provided benchmark results on a fact-checking task in which given a factual claim, the system must extract textual evidence (sets of sentences from Wikipedia pages) that support or refute the claim. In this paper, we present a completely task-agnostic pipelined system, AttentiveChecker, consisting of three homogeneous Bi-Directional Attention Flow (BIDAF) networks, which are multi-layer hierarchical networks that represent the context at different levels of granularity. We are the first to apply to this task a bi-directional attention flow mechanism to obtain a query-aware context representation without early summarization. AttentiveChecker can be used to perform document retrieval, sentence selection, and claim verification. Experiments on the FEVER dataset indicate that AttentiveChecker is able to achieve the state-of-the-art results on the FEVER test set.
Anthology ID:
N19-1230
Volume:
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)
Month:
June
Year:
2019
Address:
Minneapolis, Minnesota
Venue:
NAACL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
2218–2222
Language:
URL:
https://www.aclweb.org/anthology/N19-1230
DOI:
10.18653/v1/N19-1230
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PDF:
http://aclanthology.lst.uni-saarland.de/N19-1230.pdf