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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.
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.
The recording for this AI Seminar talk will be posted on our USC/ISI YouTube page within 1-2 business days: https://www.youtube.com/user/USCISI.