Natural Language Processing Seminar
Snigdha Chaturvedi: Modeling People in Story Generation
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Title: Modeling People in Story Generation
Abstract: Automatic story generation is the task of designing NLP systems that, given a prompt, can produce the text of a story. Most methods for this problem focus on modeling events and their coherence. However, an alternate perspective to story generation can be from the viewpoint of people described in the story. In this talk, I describe three aspects of modeling people in story generation — modeling emotions, social relationships, and social bias. In the first part of the talk, I describe our story generation approach to incorporate a desired emotion arc for the protagonist. We use Reinforcement Learning to encourage the generation model to adhere to the desired emotion arc and show that our approach results in stories that fit the emotion arc while maintaining their coherence. In the second part of the talk, I describe our story generation approach to incorporate a desired social network demonstrating relationships between various people to be mentioned in the story. We propose a model that uses latent variables to incorporate social relationships. Apart from generating coherent stories that reflect the desired social network, the latent variable-based design results in an explainable generation process. In the last part of the talk, I describe our work on uncovering gender biases exhibited implicitly in machine-generated stories. We use commonsense reasoning to reveal implicit biases associated with the protagonist’s portrayal, motivations, and mental states.
Bio: Snigdha Chaturvedi is an Assistant Professor of Computer Science at the University of North Carolina, Chapel Hill. She specializes in Natural Language Processing with an emphasis on narrative-like and socially aware understanding, summarization, and generation of language. Previously, she was an Assistant Professor at UC-Santa Cruz, and a postdoctoral fellow at UIUC and UPenn working with Dan Roth. She earned her Ph.D. in Computer Science from UMD in 2016, where she was advised by Hal Daume III. Her research has been supported by NSF, Amazon, and IBM.
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