Training classifiers with natural language explanations
Training classifiers with natural language explanations Hancock et al., ACL’18
We looked at Snorkel earlier this week, which demonstrates that maybe AI isn’t going to take over all of our programming jobs. Instead, we’ll be writing labelling functions to feed the machine! Perhaps we could call this task label engineering. To me, it feels a bit like programming a quick-and-dirty expert system, where the downstream generative model deals with all the inaccuracies and inconsistencies so that we don’t have to be perfect, just useful. Given the success of the approach, a natural question to ask is how we can enable end users to more easily create useful labelling functions in their domain. This is where BabbleLabble comes in!
In this work, we propose BabbleLabble, a framework for training classifiers in which an annotator provides a natural language explanation for each labeling decision. A semantic parser converts these explanations into programmatic labeling functions that generate noisy labels for an arbitrary amount of unlabeled data, which is used to train a classifier.
So much for those programming jobs ! 
Working with BabbleLabble, it takes users about twice as long per example to provide a label plus an explanation as it does just Continue reading
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