Natural Logic-guided Autoregressive Multi-hop Document Retrieval for Fact Verification
Rami Aly, Andreas Vlachos

TL;DR
This paper introduces a natural logic-guided autoregressive multi-hop document retrieval method for fact verification, reducing memory usage and improving stability over existing approaches by dynamically terminating retrieval based on evidence sufficiency.
Contribution
It presents a novel retrieve-and-rerank approach guided by natural logic, which is more memory-efficient and stable than current dense retrieval methods.
Findings
Competitive performance on FEVER, HoVer, FEVEROUS-S datasets
Uses 5 to 10 times less memory than state-of-the-art methods
Improved stability and human interpretability of retrieval decisions
Abstract
A key component of fact verification is thevevidence retrieval, often from multiple documents. Recent approaches use dense representations and condition the retrieval of each document on the previously retrieved ones. The latter step is performed over all the documents in the collection, requiring storing their dense representations in an index, thus incurring a high memory footprint. An alternative paradigm is retrieve-and-rerank, where documents are retrieved using methods such as BM25, their sentences are reranked, and further documents are retrieved conditioned on these sentences, reducing the memory requirements. However, such approaches can be brittle as they rely on heuristics and assume hyperlinks between documents. We propose a novel retrieve-and-rerank method for multi-hop retrieval, that consists of a retriever that jointly scores documents in the knowledge source and…
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