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.

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.

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