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First provide a summary of the paper, and then address the following criteria: Quality, clarity, originality and significance. The paper presents a method for learning multiple tasks in parallel where at each round a sample is given per each task, but only a single task can have its sample annotated. The authors formulate their method using a trade-off between exploitation and exploration. Th1 provides an upper bound on the expected cumulative number of mistakes. The algorithm is compared to 2 different approaches for choosing the single sample/task to be annotated. It is well written and provides good theoretical as well as experimental results.