Modeling Key Narrative Elements for Automatic Story Generation скачать в хорошем качестве

Modeling Key Narrative Elements for Automatic Story Generation

Date Presented: January 20, 2022 Speakers: Faeze Brahman, (UCSC) Abstract: Narratives are central to how humans reason, make sense of their experiences and communicate. In essence, narratives are a rich source of day-to-day knowledge and preserve many social and moral norms. Instilling human-like communication, commonsense knowledge, and reasoning capabilities in machines by generating coherent and consistent stories has been a long-standing challenge for AI systems. Despite human-level fluency, the generated stories by recent PLMs tend to be off-topic, not engaging enough, or contain unfaithful information. Towards generating stories with global cohesion, I aim to add controllability as well as modeling and incorporating key narrative elements that contribute to a good story, such as plot, characters, emotions, etc. I will discuss human-in-the-loop story generation using a content-inducing approach to build the “plot” incrementally. Next, I will present a new dataset and tasks for modeling and understanding “characters” in narratives as another key element. I also discuss modeling the “emotional development” of characters in neural storytelling. I will conclude with a discussion on future challenges and directions. Speaker Bio: Faeze Brahman is a Ph.D. candidate at the University of California, Santa Cruz in Computer Science. Previously, she interned at Microsoft Research, working on controllable grounded text generation; at AI2, working on unsupervised rationale generation for non-monotonic reasoning; and at Xerox PARC on RFP response assistant system. She is broadly interested in natural language understanding and generation with the long-term goal of instilling human-like communication, commonsense knowledge, and reasoning capabilities in machines. Her current research interests include (controllable) text generation, (social) commonsense reasoning, and unsupervised learning.