Towards Knowledge-Grounded Natural Language Understanding and Generation

arXiv:2403.15364v1 Announce Type: new
Abstract: 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 …

abstract arxiv benefit benefits beyond cs.cl key knowledge language language understanding natural natural language questions research tasks thesis transformer transformer models type understanding