  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.
