报告题目:Building dialog systems with less supervision
报告摘要：Lack of data is the number one challenge in deploying end-to-end trainable dialog systems for real-world applications. This talk will cover how to use learning methods to train a good model with less supervision. We will talk about how to integrate data augmentation, intermedia scaffolds, meta-learning to move towards the next-generation data-efficient dialog systems. We will briefly describe how to ensure the safety of the deployed system as well.
报告人：Zhou Yu is an Assistant Professor at the Computer Science Department at UC Davis. She received her PhD from Carnegie Mellon University in 2017. Zhou is interested in building robust and multi-purpose dialog systems using fewer data points and less annotation. She is also passionate about language generation. Zhou's work PersuasionForGood received an ACL 2019 best paper nomination recently. Zhou was featured in Forbes as 2018 30 under 30 in Science for her work on multimodal dialog systems. Her team recently won the 2018 Amazon Alexa Prize on building an engaging social bot for a $500,000 award.