Постериорная калибровка

Many models in natural language process- ing define probabilistic distributions over linguistic structures. We argue that (1) the quality of a model’s posterior distribu- tion can and should be directly …...

AbstractModern NLP classifiers are known to return uncalibrated estimations of class posteriors. Existing methods for posterior calibration rescale the predicted probabilities but often have an adverse impact on final classification …...