TRAINING CALL ROUTING APPLICATIONS BY REUSING SEMANTICALLY-LABELED DATA COLLECTED FOR PRIOR APPLICATIONS

Techniques disclosed herein include systems and methods for reusing semantically-labeled data collected for previous or existing call routing applications. Such reuse of semantically-labeled utterances can be used for automating and accelerating application design as well as data transcription and labeling for new and future call routing applications. Such techniques include using a semantic database containing transcriptions and semantic labels for several call routing applications along with corresponding baseline routers trained for those applications. This semantic database can be used to derive a semantic similarity measure between any pair of utterances, such as transcribed sentences. A mathematical model predicts how semantically related two utterances are, such as by identifying a same user intent to identifying completely unrelated intents. Such a semantic similarity measure can be used for various tasks including semantic-based example selection for language model and router training, and semantic data clustering for semi-automated labeling.