Reinforcement Learning for Improved Low Resource Dialogue Generation

Abstract In this thesis, I focus on language independent methods of improving utterance understanding and response generation and attempt to tackle some of the issues surrounding current systems. The aim is to create a unified approach to dialogue generation inspired by developments in both goal oriented and open ended dialogue systems. The main contributions in this thesis are: 1) Introducing hybrid approaches to dialogue generation using retrieval and encoder-decoder architectures to produce fluent but precise utterances in dialogues, 2) Proposing supervised, semi-supervised and Reinforcement Learning methods for domain adaptation in goal oriented dialogue and 3) Introducing models that can adapt cross lingually.