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.
翻译:对话系统可以通过一系列文本搜索,找到与用户请求相关的信息,特别是在遇到无法手动处理的请求时。神经密集检索或重新排位的最先进的技术涉及具有数亿参数的深层次学习模式,然而,使这些模式在工业规模上运作既困难又昂贵,特别是对于常常需要支持大量个人定制对话系统的云服务而言,每个系统都有自己的文本。我们报告了我们关于使先进的神经密集检索系统能够在规模上以相对廉价的硬件有效运行的工作。我们比较了领先的替代工业解决方案,并表明我们能够提供有效、快速和具有成本效益的解决办法。