Yi-Chia Wang
Yi-Chia Wang is a research scientist at Uber AI, focusing on the conversational AI. She received her Ph.D. from the Language Technologies Institute in School of Computer Science at Carnegie Mellon University. Her research interests and skills are to combine language processing technologies, machine learning methodologies, and social science theories to statistically analyze large-scale data and model human-human / human-bot behaviors. She has published more than 20 peer-reviewed papers in top-tier conferences/journals and received awards, including the CHI Honorable Mention Paper Award, the CSCW Best Paper Award, and the AIED Best Student Paper Nomination.
Engineering Blog Articles
Introducing the Plato Research Dialogue System: A Flexible Conversational AI Platform
The Plato Research Dialogue System enables experts and non-experts alike to quickly build, train, and deploy conversational AI agents.
COTA: Improving Uber Customer Care with NLP & Machine Learning
In this article, Uber Engineering introduces our Customer Obsession Ticket Assistant (COTA), a new tool that puts machine learning and natural language processing models in the service of customer care to help agents deliver improved support experiences.
Research Papers
Collaborative Multi-Agent Dialogue Model Training Via Reinforcement Learning
A. Papangelis, Y.-C. Wang, P. Molino, G. Tur
We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online. [...] [PDF]
Special Interest Group on Discourse and Dialogue (SIGDIAL), 2019
We present the first complete attempt at concurrently training conversational agents that communicate only via self-generated language. Using DSTC2 as seed data, we trained natural language understanding (NLU) and generation (NLG) networks for each agent and let the agents interact online. [...] [PDF]
Special Interest Group on Discourse and Dialogue (SIGDIAL), 2019
COTA: Improving the Speed and Accuracy of Customer Support through Ranking and Deep Networks
P. Molino, H. Zheng, Y.-C. Wang
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. [...] [PDF]
ACM SIGKDD International Conference on Knowledge Discovery and Data Science (KDD), 2018
For a company looking to provide delightful user experiences, it is of paramount importance to take care of any customer issues. This paper proposes COTA, a system to improve speed and reliability of customer support for end users through automated ticket classification and answers selection for support representatives. [...] [PDF]
ACM SIGKDD International Conference on Knowledge Discovery and Data Science (KDD), 2018
Can You be More Polite and Positive? Infusing Social Language into Task-Oriented Conversational Agents
Y.-C. Wang, R. Wang, G. Tur, H. Williams
Goal-oriented conversational agents are becoming ubiquitous in daily life for tasks ranging from personal assistants to customer support systems. For these systems to engage users and achieve their goals in a more natural manner, they need to not just provide informative replies and guide users through the problems but also to socialize with users. To this end, we extend the line of style transfer research on developing generative deep learning models to control for a specific style such as sentiment and personality. [...] [PDF]
Conversational Intelligence Challenge at Conference on Neural Information Processing Systems (ConvAI @ NeurIPS), 2018
Goal-oriented conversational agents are becoming ubiquitous in daily life for tasks ranging from personal assistants to customer support systems. For these systems to engage users and achieve their goals in a more natural manner, they need to not just provide informative replies and guide users through the problems but also to socialize with users. To this end, we extend the line of style transfer research on developing generative deep learning models to control for a specific style such as sentiment and personality. [...] [PDF]
Conversational Intelligence Challenge at Conference on Neural Information Processing Systems (ConvAI @ NeurIPS), 2018
