THESIS
2023
1 online resource (x, 49 pages) : illustrations (some color)
Abstract
The rapid proliferation of misinformation on the internet has led to an increased need
for reliable and efficient fact verification systems. In this thesis, we propose a novel fact
verification system that leverages semantic graphs to enhance reasoning in multi-evidence
situations. Our system performs reasoning by joining semantic graphs of various retrieved
evidence sentences from a trusted fact corpus. Additionally, our system combines semantic
graphs from different frameworks to jointly make claim verification predictions, providing
a more comprehensive understanding of the underlying facts. We also incorporate a language
model-based retrieval model to improve the retrieval of relevant evidence sentences,
further enhancing the overall performance. Experimental results on the FEVER da...[
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The rapid proliferation of misinformation on the internet has led to an increased need
for reliable and efficient fact verification systems. In this thesis, we propose a novel fact
verification system that leverages semantic graphs to enhance reasoning in multi-evidence
situations. Our system performs reasoning by joining semantic graphs of various retrieved
evidence sentences from a trusted fact corpus. Additionally, our system combines semantic
graphs from different frameworks to jointly make claim verification predictions, providing
a more comprehensive understanding of the underlying facts. We also incorporate a language
model-based retrieval model to improve the retrieval of relevant evidence sentences,
further enhancing the overall performance. Experimental results on the FEVER dataset
demonstrate that our approach outperforms existing fact verification systems, especially
in cases where multiple pieces of evidence are required for verification. Ablation studies
are conducted to analyze the contributions of different components within our system.
This work not only contributes to the advancement of fact verification methods but also
lays the groundwork for future research in the area of semantic graph-based reasoning
systems.
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