Fall 2019

Date

Event

Speaker

Abstract/Details

Abstract: Event structure decomposition is integral to machine reading comprehension, dialog state tracking, and other natural language understanding tasks where models of entity states and temporality as well as of event-event, participant-event, and participant-participant relations are desirable. Previous work in event representation has yet to show how organizing conceptual structures into well-defined, flexible units representing both background knowledge of the world and how that knowledge is expressed in a certain language might improve AI reasoning capabilities. In preparation for my dissertation research into this question, in this talk I will argue for a fine-grained approach to event representation based on theoretical work done in cognitive semantics: force dynamics (Talmy 1988; Croft 2012), a image schematic model of causation that my research may show can support common-sense reasoning applications. Event representation is often done at the macro-level, with a focus on sequences of events in narratives, procedures, etc.; in contrast, at a micro-level we examine subevents of clausal events by employing the entity-centric, fine granularity of a force-dynamic approach to characterize participant interaction as a function of time, qualitative change, and transmission of force. As a proof of concept linking micro- and macro-level analyses, I propose designing, implementing, and evaluating a computational model for the extraction of participant histories as storylines from scientific, procedural text. One task will be to compare how well force-dynamic event structure can be extracted using a symbolic approach based on a model of argument structure and verb classes versus a neural approach using pre-trained language models. A second task will be to examine the use of dynamic knowledge graphs as a representation for evolving fine-grained storyline event structure and the tracking of entity states. A third task will be to examine the applicability of image schemas for effect prediction via a generative process with force-dynamic structures as priors. The primary hypotheses of the study will be examined along with a tentative timeline for how my research may progress.

Bio: Michael Regan is a PhD student in Linguistics and MS student in Computer Science at the University of New Mexico, and a Professional Research Assistant in Computer Science at the University of Colorado Boulder. His research interests include cognitive semantics, event structure representation, multilingual NLP, and representation learning.