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。

GERE: 用于事实验证的生成式证据检索

GERE: Generative Evidence Retrieval for Fact Verification

Our objective is video retrieval based on natural language queries. In
addition, we consider the analogous problem of retrieving sentences or
generating descriptions given an input video. Recent work has addressed the
problem by embedding visual and textual inputs into a common space where
semantic similarities correlate to distances. We also adopt the embedding
approach, and make the following contributions: First, we utilize web image
search in sentence embedding process to disambiguate fine-grained visual
concepts. Second, we propose embedding models for sentence, image, and video
inputs whose parameters are learned simultaneously. Finally, we show how the
proposed model can be applied to description generation. Overall, we observe a
clear improvement over the state-of-the-art methods in the video and sentence
retrieval tasks. In description generation, the performance level is comparable
to the current state-of-the-art, although our embeddings were trained for the
retrieval tasks.

该研究旨在基于自然语言查询进行视频检索，并采用嵌入模型进行检索任务的训练，试图通过图像搜索以及嵌入模型的应用使 fine-grained 视觉概念得到消歧，最终在视频和句子检索任务中实现了明显的改进，并取得了与当前最先进技术相媲美的描述生成性能。