对话系统中快速轻量级的答案文本检索

H. Wan, S. Patel, J. William Murdock, Saloni Potdar, Sachindra Joshi
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引用次数: 0

摘要

对话系统可以从搜索文本语料库以查找与用户请求相关的信息中获益,特别是在遇到无法获得手动策划响应的请求时。最先进的神经密集检索或重新排序技术涉及具有数亿个参数的深度学习模型。然而,让这样的模型在工业规模上运行是困难和昂贵的,特别是对于经常需要支持大量单独定制的对话系统的云服务,每个对话系统都有自己的文本语料库。我们报告了我们的工作,使先进的神经密集检索系统在相对廉价的硬件上有效地大规模运行。我们与领先的替代工业解决方案进行比较,并表明我们可以提供有效,快速和具有成本效益的解决方案。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Fast and Light-Weight Answer Text Retrieval in Dialogue Systems
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.
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