Fact verification (FV) is a challenging task which aims to verify a claim
using multiple evidential sentences from trustworthy corpora, e.g., Wikipedia.
Most existing approaches follow a three-step pipeline framework, including
document retrieval, sentence retrieval and claim verification. High-quality
evidences provided by the first two steps are the foundation of the effective
reasoning in the last step. Despite being important, high-quality evidences are
rarely studied by existing works for FV, which often adopt the off-the-shelf
models to retrieve relevant documents and sentences in an
"index-retrieve-then-rank" fashion. This classical approach has clear drawbacks
as follows: i) a large document index as well as a complicated search process
is required, leading to considerable memory and computational overhead; ii)
independent scoring paradigms fail to capture the interactions among documents
and sentences in ranking; iii) a fixed number of sentences are selected to form
the final evidence set. In this work, we propose GERE, the first system that
retrieves evidences in a generative fashion, i.e., generating the document
titles as well as evidence sentence identifiers. This enables us to mitigate
the aforementioned technical issues since: i) the memory and computational cost
is greatly reduced because the document index is eliminated and the heavy
ranking process is replaced by a light generative process; ii) the dependency
between documents and that between sentences could be captured via sequential
generation process; iii) the generative formulation allows us to dynamically
select a precise set of relevant evidences for each claim. The experimental
results on the FEVER dataset show that GERE achieves significant improvements
over the state-of-the-art baselines, with both time-efficiency and
memory-efficiency.

该论文提出了 GERE，一个通过生成方式检索证据的系统，以解决事实验证中的技术难题，即减少存储和计算成本，捕捉文档和句子之间的依赖关系，动态地选择每个索赔的相关证据，超越现有技术基线。