Towards Knowledge-Grounded Natural Language Understanding and Generation
Chenxi Whitehouse
This thesis investigates how natural language understanding and generation
with transformer models can benefit from grounding the models with knowledge
representations and addresses the following key research questions: (i) Can
knowledge of entities extend its benefits beyond entity-centric tasks, such as
entity linking? (ii) How can we faithfully and effectively extract such
structured knowledge from raw text, especially noisy web text? (iii) How do
other types of knowledge, beyond structured knowledge, contribute to improving
NLP tasks?
  Studies in this thesis find that incorporating relevant and up-to-date
knowledge of entities benefits fake news detection, and entity-focused
code-switching significantly enhances zero-shot cross-lingual transfer on
entity-centric tasks. In terms of effective and faithful approaches to
extracting structured knowledge, it is observed that integrating negative
examples and training with entity planning significantly improves performance.
Additionally, it is established that other general forms of knowledge, such as
parametric and distilled knowledge, enhance multimodal and multilingual
knowledge-intensive tasks. This research shows the tangible benefits of diverse
knowledge integration and motivates further exploration in this direction.