UC San Diego

The recent proliferation of machine learning (ML) agents that interact with humans through natural text conversation (e.g. smart phone assistants, chat bots) is predicated on the unprecedented public availability of user-contributed text (e.g. blogs, product reviews) and behavioral traces (e.g. purchases, social media interactions).Current methods for building conversational agents have seen success in highly structured fields like automated help desks and reservation booking. However, it remains challenging to apply these ML systems to help users with daily tasks in more natural and intuitive ways. For example, current recommender systems cannot fluidly engage with users for multiple rounds of conversation.

In this dissertation we focus primarily on developing technologies that allow intelligent agents to engage with users in trustworthy, personalized, and interactive ways through the medium of text.Specifically, my work focuses on 1) explainable dialog models to facilitate meaningful interviews; 2) a language modeling framework to infer user preferences from dialog; and 3) a bot-play framework for training explainable and personalized recommender systems to understand and reflect user feedback over multiple turns of conversation. I finally present two case studies on applying the aforementioned technologies to build personalized interactive agents that generate and edit instructional texts (e.g. cooking recipes) to assist users in their daily lives.