Applying Multiclass Bandit algorithms to call-type classification

We analyze the problem of call-type classification using data that is weakly labelled. The training data is not systematically annotated, but we consider we have a weak or lazy oracle able to answer the question “Is sample x of class q?” by a simple ‘yes’ or ‘no’ answer. This situation of learning might be encountered in many real-world problems where the cost of labelling data is very high. We prove that it is possible to learn linear classifiers in this setting, by estimating adequate expectations inspired by the Multiclass Bandit paradgim. We propose a learning strategy that builds on Kessler's construction to learn multiclass perceptrons. We test our learning procedure against two real-world datasets from spoken langage understanding and provide compelling results.