Recent neural retrieval mainly focuses on ranking short texts and is
challenged with long documents. Existing work mainly evaluates either ranking
passages or whole documents. However, there are many cases where the users want
to find a relevant passage within a long document from a huge corpus, e.g.
legal cases, research papers, etc. In this scenario, the passage often provides
little document context and thus challenges the current approaches to finding
the correct document and returning accurate results. To fill this gap, we
propose and name this task Document-Aware Passage Retrieval (DAPR) and build a
benchmark including multiple datasets from various domains, covering both DAPR
and whole-document retrieval. In experiments, we extend the state-of-the-art
neural passage retrievers with document-level context via different approaches
including prepending document summary, pooling over passage representations,
and hybrid retrieval with BM25. The hybrid-retrieval systems, the overall best,
can only improve on the DAPR tasks marginally while significantly improving on
the document-retrieval tasks. This motivates further research in developing
better retrieval systems for the new task. The code and the data are available
at this https URL

本文提出了文档感知段落检索 (DAPR) 任务的概念，并通过在段落表示中引入文档级上下文的不同方法扩展最先进的神经段落检索器。实验结果表明，混合检索系统在 DAPR 任务中只能略微改进，但能显著改进文档检索任务，这激励着进一步研究该新任务的更好检索系统的开发。

DAPR: 一个关于文档感知的片段检索基准

DAPR: A Benchmark on Document-Aware Passage Retrieval

Pyserini is an easy-to-use Python toolkit that supports replicable IR
research by providing effective first-stage retrieval in a multi-stage ranking
architecture. Our toolkit is self-contained as a standard Python package and
comes with queries, relevance judgments, pre-built indexes, and evaluation
scripts for many commonly used IR test collections. We aim to support, out of
the box, the entire research lifecycle of efforts aimed at improving ranking
with modern neural approaches. In particular, Pyserini supports sparse
retrieval (e.g., BM25 scoring using bag-of-words representations), dense
retrieval (e.g., nearest-neighbor search on transformer-encoded
representations), as well as hybrid retrieval that integrates both approaches.
This paper provides an overview of toolkit features and presents empirical
results that illustrate its effectiveness on two popular ranking tasks. We also
describe how our group has built a culture of replicability through shared
norms and tools that enable rigorous automated testing.

本文介绍了一个名为 Pyserini 的 Python 工具包，支持信息检索、稀疏检索以及密集检索，并在两个流行的排名任务中验证了其有效性。