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Can anybody please explain multitask learning in simple and intuitive way? May be some real
world problem would be useful.Mostly, these days i am seeing many people are using it for natural language processing tasks.
Let’s say you’ve built a sentiment classifier for a few different domains. Say, movies, music DVDs, and electronics. These are easy to build high quality classifiers for, because there is tons of training data that you’ve scraped from Amazon. Along with each classifier, you also build a similarity detector that will tell you for a given piece of text, how similar it was to the dataset each of the classifiers was trained on.
Now you want to find the sentiment of some text from an unknown domain or one in which there isn’t such a great dataset to train on. Well, how about we take a similarity weighted combination of the classifications from the three high quality classifiers we already have. If we are trying to classify a dish washer review (there is no giant corpus of dish washer reviews, unfortunately), it’s probably most similar to electronics, and so the electronics classifier will be given the most weight. On the other hand, if we are trying to classify a review of a TV show, probably the movies classifier will do the best job.