PHOEBE LIU, Senior Data Scientist, Appen

The development of conversational agents that can interact with people in the real world presents many difficult challenges. First, natural language understanding and perception of context must be performed based on continuous data from sensors that often contain a high rate of sensor noise. Second, conversational agents must respond appropriately to natural human behavior that spans vast action spaces, thus data-efficient collection strategies are necessary in order to acquire large amounts of training data. In this talk, I will address both sides of the challenge: (1) using data-efficient strategies during the utterance collection and annotation phases to optimize the trade-off between cost and quality when collecting training data, (2)
using data-driven approaches to train and generate behaviors for a conversational agent despite noisy data and lack of training labels. Finally, I will discuss field studies and past research for a conversational humanoid robot to autonomously generate socially appropriate locomotion and speech behaviors that was deployed in the real world to provide guidance and services to people.

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