Building Interpretable and Reliable Open Information Retriever for New
  Domains Overnight
Xiaodong Yu Ben Zhou Dan Roth
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.