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Vocabulary mismatch is a central problem in information retrieval (IR), i.e.,the relevant documents may not contain the same (symbolic) terms of the query.Recently, neural representations have shown great success in capturing semanticrelatedness, leading to new possibilities to alleviate the vocabulary mismatchproblem in IR. However, most existing efforts in this direction have beendevoted to the re-ranking stage. That is to leverage neural representations tohelp re-rank a set of candidate documents, which are typically obtained from aninitial retrieval stage based on some symbolic index and search scheme (e.g.,BM25 over the inverted index). This naturally raises a question: if therelevant documents have not been found in the initial retrieval stage due tovocabulary mismatch, there would be no chance to re-rank them to the toppositions later. Therefore, in this paper, we study the problem how to employneural representations to improve the recall of relevant documents in theinitial retrieval stage. Specifically, to meet the efficiency requirement ofthe initial stage, we introduce a neural index for the neural representationsof documents, and propose two hybrid search schemes based on both neural andsymbolic indices, namely the parallel search scheme and the sequential searchscheme. Our experiments show that both hybrid index and search schemes canimprove the recall of the initial retrieval stage with small overhead.

Beyond Precision: A Study on Recall of Initial Retrieval with Neural Representations