Understanding Natural Language with Commonsense Knowledge Representation, Reasoning, and Simulation
Year: 2020
Type: dissertation
Abstract: For machines to understand language, they must intuitively grasp the commonsense knowledge that underlies the situations they encounter in text.A simple statement such as it is raining immediately implies a bank of shared context for any human reader: they should bring an umbrella, roads will be slippery, increased traffic may make them late, rain boots are preferable to sandals, and many more.Language understanding systems must be able to robustly use this commonsense knowledge to make decisions or take actions.Observations of the world are always more rich and detailed than the information that is explicitly transmitted through language, and machines must be able to fill in remaining details with commonsense inferences.Recent advances in natural language processing have made considerable progress in identifying the commonsense implications of situations described in text.These methods generally involve training highparameter language models on large language corpora and have shown marked improvement on a variety of benchmark end tasks in natural language understanding.However, these systems are brittle -often failing when presented with out-of-distribution inputs -and uninterpretable -incapable of providing insights into why these different inputs cause shifted behavior.Meanwhile, traditional approaches to natural language understanding, which focus on linking language to background knowledge from large ontologies, remain iii First and foremost, I'd like to thank my advisor, Yejin Choi, for her counsel, support, and feedback over the last six years.Her mentorship and advising were crucial for my growth as a researcher.And her guidance and flexibility allowed me to explore challenging problems in commonsense representation and reasoning for NLP, and learn to develop applicable solutions for them.I'm also incredibly grateful to Asli Çelikyilmaz, a friend and colleague, who advised me throughout my stay as a student researcher at Microsoft, and who motivated my passion for NLP topics in language generation.I'd also like thank Peter Clark and Oren Etzioni,
Author: Antoine Bosselut
Language: English
Topic: Topic Modeling
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W3161793849