1:00-1:30PM:
John Franklin - Hunting for Helen: A Lost Storytelling Engine in Greek Epic Poetry.

This paper will present and analyze a collection of storytelling motifs and variants drawn from ancient Greek epic poetry. Sources include key passages of Homer’s Odyssey, and various later authors who preserve plot summaries and story fragments that derive from several lost epic poems. The material derives from, I argue, an early narrative subsystem of the genre that has survived in various vestigial forms. I shall attempt to reduce the variants to a single narrative ‘engine’ that allowed oral formulaic singers to generative wandering adventures, akin to those of Odysseus, but set in the Eastern Mediterranean; it centered especially on the possession of Helen of Sparta/Troy, either an effort by her husband Menelaos to recapture her immediately after her abduction by the Trojan prince Paris; or wandering travels in the same area with Menelaos after being recaptured from Troy.

2:45-3:45PM:
Mohit Iyyer - Towards Understanding Narratives with Artificial Intelligence

Abstract: 
One of the fundamental goals of artificial intelligence is to build computers that understand language at a human level. Recent progress towards this goal has been fueled by deep learning, which represents words, sentences, and even documents with learned vectors of real-valued numbers. However, creative language—the sort found in novels, film, and comics—poses an immense challenge for such models because it contains a wide range of linguistic phenomena, from phrasal and sentential syntactic complexity to high-level discourse structures such as narrative and character arcs. In this talk, I discuss our recent work on applying deep learning to creative language understanding, as well as exploring the challenges that must be solved before further progress can be made. I begin with an general overview of deep learning before presenting model architectures for two tasks involving creative language understanding: 1) modeling dynamic relationships between fictional characters in novels, and 2) predicting dialogue and artwork from comic book panels. For both tasks, our models only achieve a surface-level understanding, limited by a lack of world knowledge, an inability to perform commonsense reasoning, and a reliance on huge amounts of data. I conclude by proposing ideas on how to push these models to produce deeper insights from creative language that might be of use to humanities researchers. 

Bio: 
Mohit Iyyer is an assistant professor in computer science at the University of Massachusetts, Amherst. His research focuses on designing deep neural networks for both traditional natural language processing tasks (e.g., question answering, sentiment analysis) and new problems that involve understanding creative language (e.g., modeling fictional narratives and characters). He received his PhD in computer science from the University of Maryland, College Park in 2017 and spent the past year as a researcher at the Allen Institute for Artificial Intelligence. 

4:00-5:00PM:
Mirta Galesic - Communicating Complex Data to the Public

To make informed decisions about health, environmental, and financial issues, people often need to understand complex data. Narratives have been shown to be more persuasive in communicating such data than mere statements of facts. However, narratives without facts can be deceiving. I will describe extant research on best ways to enrich narratives with factual information using numerical and graphical formats, analogies, social context, and moral values.