Thesis / Dissertation

Memory-augmented neural networks for better discourse understanding

Fei Liu, Timothy Baldwin (ed.)

Published : 2019

Abstract

Discourse understanding, due to the multi-sentence nature of discourse, requires consideration of larger contexts, capturing long-range dependencies, and modelling the interactions of entities. While conventional models are unable to keep information stably over long timescales, memory-augmented models are better capable of storing and accessing knowledge, making them well-suited for discourse. In this thesis, we introduce a number of methods for improving memory-augmented models to better understand discourse, validating the utility of memory and establishing a firm base for future studies to build upon.