UCLA

Teaching machines to speak and act like a human is one of the longest-running goals in Artificial Intelligence. This thesis tackles two important problems in building the next generation of dialogue systems: enhancing the emotional intelligence of social chatbots and learning semantic structures from dialogue corpus. On the one hand, taking into account emotional quotient in dialogue system design help machine to mimic human behavior and further improve the long-term user engagement. On the other hand, extracting structural information from dialogue data is critical for us to analyze user behavior and system performance. The technology could be applied to various areas in computational linguistics, such as dialogue management, discourse analysis, and dialogue summarization.

This thesis consists of two parts. In the first part, we aim to present our efforts at studying emotional intelligence in dialogues systems. We break down the problem into three subjects: 1) the modeling and incorporation of human values, i.c., people tend to have common attitudes towards some statements or scenarios; 2) the inference of social relations between interlocutors from dialogues. Chatbots with such inferring capability can understand human behavior better and act appropriately; and 3) the modeling and tracking of speakers' mental states. This is beyond understanding what users say to perceive what users imply, requiring agents to mentally simulate the evolution status of the environment.

In the second part of this thesis, we investigate how we can extract structural information from dialogue corpus. In particular, we pioneered two research directions: 1) we reconstruct the original dialogues with variational recurrent neural networks and structured attention, then we extract the structure by computing the transitions between latent states; and 2) we detect and track the status of potential slot token groups to approximate a representation of task-oriented dialogue structures. We explored the problem from both theoretical and practical perspectives.