Training Conversational Agents on Noisy Data

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Presented by Appen

Appen collects and labels images, text, speech, audio, video, and other data used to build and continuously improve the world’s most innovative arti­ficial intelligence systems.

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This session 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.

Phoebe Liu

Phoebe Liu is a senior data scientist with expertise in robotics and conversational AI. Previously, she was a robotics researcher in Japan, working in Hiroshi

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