Towards Interactive Story Generation

When:
Thursday, July 16, 2020, 11:00 am - 12:00 pm PDTiCal
Where:
https://usc.zoom.us/j/93857321879?pwd=SHMybGxiWDlzeW1XVllsOC9EdndHZz09
This event is open to the public.
Type:
NL Seminar
Speaker:
Mohit Iyyer (UMass Amherst)
Description:

VTC Link:

https://usc.zoom.us/j/93857321879?pwd=SHMybGxiWDlzeW1XVllsOC9EdndHZz09

Meeting ID: 938 5732 1879

Password: 073790

Abstract: Story generation is difficult to computationally formalize and evaluate, and there are many important questions to ask when tackling the problem. What should we consider as the base unit of a story (e.g., a sentence? a paragraph? a chapter?) What kind of data should we use to train these models (novels? short stories? overly simplistic mechanically-turked paragraphs?) Is any model architecture currently capable of producing long-form narratives that have some semblance of coherent discourse structure, such as plot arcs and character development? When evaluating the outputs of our models, can we do better than just asking people to rate the text based on vaguely defined properties such as "enjoyability"? In this talk, I'll discuss my lab's ongoing work on story generation by introducing a new dataset and evaluation method that we hope will spur progress in this area, and also describing fine-tuning strategies for large-scale Transformers that produce more coherent and stylistically-consistent stories. A major bottleneck of these models is their memory and speed inefficiency; as such, I'll conclude by discussing heavily-simplified Transformer language models that make training less expensive without sacrificing output quality.

Bio: Mohit Iyyer is an assistant professor in computer science at the University of Massachusetts Amherst. His research focuses broadly on designing machine learning models for discourse-level language generation (e.g., for story generation and machine translation), and his group also works on tasks involving creative language understanding (e.g., modeling fictional narratives and characters). He is the recipient of best paper awards at NAACL (2016, 2018) and a best demo award at NeurIPS 2015. He received his PhD in computer science from the University of Maryland, College Park in 2017, advised by Jordan Boyd-Graber and Hal Daumé III, and spent the following year as a researcher at the Allen Institute for Artificial Intelligence.

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