Neural Language Understanding of People's Names PolyAI

This is a deep-dive into one of the problems we face when we model dialogue: understanding mentions of people's names in a restaurant booking system. This article presents how we approached the problem and solved it using some creative neural network structures. At PolyAI, we use datasets of billions of conversations and unstructured natural language texts to learn powerful deep neural models of conversational response. These models allow us to embed any conversational context or response into a shared high-dimensional vector space, so we can retrieve relevant responses, answers, entities and even photos from large databases comprising in-domain knowledge. Comparison of embedding vectors can also facilitate intent detection, i.e. classification of spoken language into specific categories such as'make a booking' or'confirm booking'. In this way, we can exploit a large ranker model and its internal implicit semantic vector space to solve many of the problems in dialogue, without hand-designing any explicit semantic structures like dialogue acts.