Quality assurance systems based on speaker intent detection

Abstract

A semantic similarity based configurable system for automatic scenario detection in customer-agent conversations is disclosed. Tire system understands intent from the vector space semantic similarity between speaker sentences, which is agnostic to the use of synonyms and tolerates a large amount of paraphrasing. This approach scales easily to a large number of customers and can be fed more data to increase accuracy and precision. Furthermore, the system is configurable in real-time so that the client is able to control which intents are detected and how. In some embodiments, the semantic similarity based configurable system comprises a scenario detection system, a conversation tag system, a bi-encoder, and a cross-encoder, where the scenario detection system receives inputs of sample phrases and customer-agent utterances and generates results. Tire sample phrases may be phrases and keywords that describe a scenario expressing the behavior of a customer or call agent.