Computing and Information Systems - Theses

Permanent URI for this collection

Search Results

Now showing 1 - 1 of 1
  • Item
    Thumbnail Image
    Memory-augmented neural networks for better discourse understanding
    Liu, Fei ( 2019)
    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.