Intent Disambiguation for Task-oriented Dialogue Systems

Task-Oriented Dialogue Systems (TODS) have been widely deployed for domain specific virtual assistants at contact centres to route customers' calls or deliver information needs of the customer in a conversational interaction. TODS employ natural language understanding components in order to map user commands to a set of pre-defined intents. However, Contact Centre users often fail to formulate their complex information needs in a single utterance which leads to formulating ambiguous user commands. This can negatively impact intent classification, and consequently customer satisfaction. To avoid feeding ambiguous user commands to the intent classifier of virtual assistants and help users in formulating their commands, we have implemented a solution that (1) identifies when a user is ambiguous and the virtual assistant should ask a clarification question, (2) disambiguates the user command and provides top-N most likely intents in a form of a clarification question. Our experimental result shows that our proposed intent disambiguation solution has a statistically significant improvement over a popularity based intent disambiguation model and an Intent Ranking Model of the Natural Language Understanding engine for a virtual assistant of a contact centre in terms of intent disambiguation accuracy and Mean Reciprocal Rank.