Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
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Abstract (arXiv)
Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available. The state-of-the-art technology for neural dense retrieval or re-ranking involves deep learning models with hundreds of millions of parameters. However, it is difficult and expensive to get such models to operate at an industrial scale, especially for cloud services that often need to support a big number of individually customized dialogue systems, each with its own text corpus. We report our work on enabling advanced neural dense retrieval systems to operate effectively at scale on relatively inexpensive hardware. We compare with leading alternative industrial solutions and show that we can provide a solution that is effective, fast, and cost-efficient.
Key facts. Cited by 2 papers in the map (data through 14 Oct 2026). Builds on 28 of its 34 references that are in the map. First posted on arXiv 27 May 2022.
Builds on
28 of 34 references.
References · 28
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16 more
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