Active Learning for Dialogue Act Labelling
Active learning is a useful technique that allows for a con- siderably reduction of the amount of data we need to manually label in order to reach a good performance of a statistical model. In order to apply active learning to a particular task we need to previously define an effective selection criteria, that picks out the most informative samples at each iteration of active learning process. This is still an open problem that we are going to face in this work, in the task of dialogue anno- tation at dialogue act level. We present two different criteria, weighted number of hypothesis and entropy, that we have applied to the Sample Selection Algorithm for the task of dialogue act labelling, that retrieved appreciably improvements in our experimental approach.