Information retrieval (IR) or knowledge retrieval, is a critical component
for many down-stream tasks such as open-domain question answering (QA). It is
also very challenging, as it requires succinctness, completeness, and
correctness. In recent works, dense retrieval models have achieved
state-of-the-art (SOTA) performance on in-domain IR and QA benchmarks by
representing queries and knowledge passages with dense vectors and learning the
lexical and semantic similarity. However, using single dense vectors and
end-to-end supervision are not always optimal because queries may require
attention to multiple aspects and event implicit knowledge. In this work, we
propose an information retrieval pipeline that uses entity/event linking model
and query decomposition model to focus more accurately on different information
units of the query. We show that, while being more interpretable and reliable,
our proposed pipeline significantly improves passage coverages and denotation
accuracies across five IR and QA benchmarks. It will be the go-to system to use
for applications that need to perform IR on a new domain without much dedicated
effort, because of its superior interpretability and cross-domain performance.

提出了一种信息检索管道，利用实体 / 事件链接模型和查询分解模型来更准确地关注查询的不同信息单元，该管道显著提高了对五个信息检索和问答基准的段落覆盖率和指代准确性。由于其卓越的可解释性和跨领域性能，它将成为需要在新领域上执行信息检索而无需大量投入的应用程序的首选系统。