Business-specific Frequently Asked Questions (FAQ) retrieval in task-oriented dialog systems poses unique challenges vis-\`a-vis community based FAQs. Each FAQ question represents an intent which is usually an umbrella term for many related user queries. We evaluate performance for such Business FAQs both with standard FAQ retrieval techniques using query-Question (q-Q) similarity and few-shot intent detection techniques. Implementing a real world solution for FAQ retrieval in order to support multiple tenants (FAQ sets) entails optimizing speed, accuracy and cost. We propose a novel approach to scale multi-tenant FAQ applications in real-world context by contrastive fine-tuning of the last layer in sentence Bi-Encoders along with tenant-specific weight switching.
翻译:在以任务为导向的对话系统中,针对特定企业的常见问题检索(FAQ)对以社区为基础的常见问题提出了独特的挑战。每个常见问题都代表着一种通常对许多相关用户查询来说是一个总括术语的意图。我们用查询-问题(q-Q)相似性和几发意向探测技术来评价这类商业常见问题检索方法的性能。为了支持多个租户(FAQ),采用一个真正的世界性办法,需要优化速度、准确性和成本。我们提出了在现实世界范围内扩大多租户的FAQ应用程序的新办法,办法是对Bi-Ecockers句中的最后一层进行对比性微调,同时对租户特定重量转换。