Today we’re joined by Nasrin Mostafazadeh, Senior AI Research Scientist at New York-based Elemental Cognition.
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Our conversation focuses on Nasrin’s work in event-centric contextual modeling in language and vision, which she sees as a means of giving AI systems a bit of “common sense.” We discuss Nasrin’s work on the Story Cloze Test, which is a reasoning framework for evaluating story understanding and generation. We explore the details of this task–including what constitutes a “story”–and some of the challenges it presents and approaches for solving it. We also discuss how you model what a computer understands, building semantic representation algorithms, different ways to approach “explainability,” and multimodal extensions to her contextual modeling work.
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Mentioned in the Interview
- Elemental Cognition
- Event-centric Multimodal Context Modeling
- Story Cloze Test
- Paper: Tackling the Story Ending Biases in The Story Cloze Test
- Paper: Improving Language Understanding by Generative Pre-Training
- Paper: The Effect of Different Writing Tasks on Linguistic Style: A Case Study of the ROC Story Cloze Task
- Paper: deltaBLEU: A Discriminative Metric for Generation Tasks with Intrinsically Diverse Targets
- Paper: BLEU: a Method for Automatic Evaluation of Machine Translation
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“More On That Later” by Lee Rosevere licensed under CC By 4.0