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Beyond Accuracy: Behavioral Testing of NLP Models with CheckList

We will present CheckList, a task-agnostic methodology and tool for testing NLP models inspired by principles of behavioral testing in software engineering. We will show a lot of fun bugs we discovered with CheckList, both in commercial models (Microsoft, Amazon, Google) and research models (BERT, RoBERTA for sentiment analysis, QQP, SQuAD).

We’ll also present comparisons between CheckList and the status quo, in a case study at Microsoft and a user study with researchers and engineers. We show that CheckList is a really helpful process and tool for testing and finding bugs in NLP models, both for practitioners and researchers.

Continuous pretraining and delivery of NLP models to optimize sales engagement

Sales engagement is a complex process that involves many players and touchpoints (such as emails, phone calls, etc.) across different industries and...