Beyond Accuracy: Behavioral Testing of NLP Models with Sameer Singh - #406
The TWIML AI Podcast (formerly This Week in Machine Learning & Artificial Intelligence)3 Sep 2020

Beyond Accuracy: Behavioral Testing of NLP Models with Sameer Singh - #406

Today we’re joined by Sameer Singh, an assistant professor in the department of computer science at UC Irvine. Sameer’s work centers on large-scale and interpretable machine learning applied to information extraction and natural language processing. We caught up with Sameer right after he was awarded the best paper award at ACL 2020 for his work on Beyond Accuracy: Behavioral Testing of NLP Models with CheckList. In our conversation, we explore CheckLists, the task-agnostic methodology for testing NLP models introduced in the paper. We also discuss how well we understand the cause of pitfalls or failure modes in deep learning models, Sameer’s thoughts on embodied AI, and his work on the now famous LIME paper, which he co-authored alongside Carlos Guestrin. The complete show notes for this episode can be found at twimlai.com/go/406.

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