Testing NLP Models
The discussion highlights the importance of establishing expected behaviors for NLP models, emphasizing that tests should cover various aspects like names, negation, and coreference. By creating sanity checks and directional expectation tests, one can assess a model's robustness without needing to specify exact outputs. This approach allows for a deeper understanding of how changes in input should affect parsing and translation outcomes.In this clip
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NLP Highlights
114 - Behavioral Testing of NLP Models, with Marco Tulio Ribeiro
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