Narrative structure is an ubiquitous and intriguing phenomenon. By virtue of this structure we recognize the presence of “villainy” or “revenge” in a story, even if those words are not actually present in the text. Narrative structure is an anvil for forging new artificial intelligence and machine learning techniques, and is a window into abstraction and conceptual learning as well as into culture and its influence on cognition. Mark Finlayson, research scientist at the MIT’s Computer Science and Artificial Intelligence Lab, advances our understanding of narrative structure by describing Analogical Story Merging (ASM), a new machine learning algorithm that can extract culturally-relevant plot patterns from sets of folktales. He demonstrates that ASM can learn a substantive portion of Vladimir Propp’s influential theory of the structure of folktale plots.
Beyond folktales, the work described has several significant applications. First, it points the way toward important applications in many domains, including information retrieval, persuasion and negotiation, natural language understanding and generation, and computational creativity. Second, abstraction from natural language semantics is a skill that underlies many cognitive tasks, and so this work provides insight into those processes. Finally, the work opens the door to a computational understanding of cultural influences on cognition and understanding cultural differences as captured in stories.
Thursday, March 8
Mark Finlayson’s research focuses on representing, extracting and using higher-order semantic patterns in natural language. He received a B.S.E from the University of Michigan in 1998, and M.S. and Ph.D. degrees from MIT in 2001 and 2011, respectively, all in Electrical Engineering and Computer Science.
This invited talk is hosted by Hasan Davulcu, firstname.lastname@example.org or 480-965-6385.