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Rapid response, namely low latency, is fundamental in search applications; itis particularly so in interactive search sessions, such as those encountered inconversational settings. An observation with a potential to reduce latencyasserts that conversational queries exhibit a temporal locality in the lists ofdocuments retrieved. Motivated by this observation, we propose and evaluate aclient-side document embedding cache, improving the responsiveness ofconversational search systems. By leveraging state-of-the-art dense retrievalmodels to abstract document and query semantics, we cache the embeddings ofdocuments retrieved for a topic introduced in the conversation, as they arelikely relevant to successive queries. Our document embedding cache implementsan efficient metric index, answering nearest-neighbor similarity queries byestimating the approximate result sets returned. We demonstrate the efficiencyachieved using our cache via reproducible experiments based on TREC CAsTdatasets, achieving a hit rate of up to 75% without degrading answer quality.Our achieved high cache hit rates significantly improve the responsiveness ofconversational systems while likewise reducing the number of queries managed onthe search back-end.

Caching Historical Embeddings in Conversational Search