In [1,2], we presented a method for automatic detection of action items from natural conversation. This method relies on supervised classification techniques that are trained on data annotated according to a hierarchical notion of dialogue structure; data which are expensive and time-consuming to produce. In [3], we presented a meeting browser which allows users to view a set of automatically-produced action item summaries and give feedback on their accuracy. In this paper, we investigate methods of using this kind of feedback as implicit supervision, in order to bypass the costly annotation process and enable machine learning through use. We investigate, through the transformation of human annotations into hypothetical idealized user interactions, the relative utility of various modes of user interaction and techniques for their interpretation. We show that performance improvements are possible, even with interfaces that demand very little of their users' attention.
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