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Information retrieval (IR) or knowledge retrieval, is a critical componentfor many down-stream tasks such as open-domain question answering (QA). It isalso very challenging, as it requires succinctness, completeness, andcorrectness. In recent works, dense retrieval models have achievedstate-of-the-art (SOTA) performance on in-domain IR and QA benchmarks byrepresenting queries and knowledge passages with dense vectors and learning thelexical and semantic similarity. However, using single dense vectors andend-to-end supervision are not always optimal because queries may requireattention to multiple aspects and event implicit knowledge. In this work, wepropose an information retrieval pipeline that uses entity/event linking modeland query decomposition model to focus more accurately on different informationunits of the query. We show that, while being more interpretable and reliable,our proposed pipeline significantly improves passage coverages and denotationaccuracies across five IR and QA benchmarks. It will be the go-to system to usefor applications that need to perform IR on a new domain without much dedicatedeffort, because of its superior interpretability and cross-domain performance.

Building Interpretable and Reliable Open Information Retriever for New Domains Overnight