  This thesis investigates the controllability of deep learning-based,
end-to-end, generative dialogue systems in both task-oriented and chit-chat
scenarios. In particular, we study the different aspects of controlling
generative dialogue systems, including controlling styles and topics and
continuously adding and combining dialogue skills. In the three decades since
the first dialogue system was commercialized, the basic architecture of such
systems has remained substantially unchanged, consisting of four pipelined
basic components, namely, natural language understanding (NLU), dialogue state
tracking (DST), a dialogue manager (DM) and natural language generation (NLG).
The dialogue manager, which is the critical component of the modularized
system, controls the response content and style. This module is usually
programmed by rules and is designed to be highly controllable and easily
extendable. With the emergence of powerful "deep learning" architectures,
end-to-end generative dialogue systems have been proposed to optimize overall
system performance and simplify training. However, these systems cannot be
easily controlled and extended as the modularized dialogue manager can. This is
because a single neural system is used, which is usually a large pre-trained
language model (e.g., GPT-2), and thus it is hard to surgically change
desirable attributes (e.g., style, topics, etc.). More importantly,
uncontrollable dialogue systems can generate offensive and even toxic
responses. Therefore, in this thesis, we study controllable methods for
end-to-end generative dialogue systems in task-oriented and chit-chat
scenarios. Throughout the chapters, we describe 1) how to control the style and
topics of chit-chat models, 2) how to continuously control and extend
task-oriented dialogue systems, and 3) how to compose and control multi-skill
dialogue models.
