Abstract:
Cross-Document Fact Verification (CDFV) aims to retrieve related evidence from multiple documents to verify the factuality of a given claim, relying on the quality of the...Show MoreMetadata
Abstract:
Cross-Document Fact Verification (CDFV) aims to retrieve related evidence from multiple documents to verify the factuality of a given claim, relying on the quality of the retrieved evidence. However, existing CDFV approaches heavily depend on specific heuristics or rule-based strategies, leveraging similarity measures of semantic or surface forms between claims and documents for evidence retrieval. To address the problem above, we propose an Evidential Graph Attention neTwork (EGAT) for CDFV. EGAT utilizes graph attention network to capture relationships between sentences, updating their representations and obtaining more expressive sentence embeddings. To acquire credible evidence, EGAT leverages golden evidence which is manually annotated and capable of verifying the factuality of the claim. Sentences in the graph that are most relevant to the gold evidence are selected as evidence sentences. To enhance the reliability of claim verification, EGAT utilizes a homogeneous network to fuse the information of the claim and evidence, which makes full use of the information provided by evidence and reduces the duplication of work in the process of claim verification. Experimental results confirm the effectiveness of EGAT in retrieving credible evidence and demonstrate improvements in achieving accurate claim verification.
Date of Conference: 30 June 2024 - 05 July 2024
Date Added to IEEE Xplore: 09 September 2024
ISBN Information: