Semi-Supervised Learning For Information Extraction From Dialogue

In this work we present a method for semi-supervised learning from transcripts of dialogue between humans. We consider the scenario in which a large amount of transcripts are available, and we would like to extract some semantic information from them; however, only a small number of transcripts have been labeled with this information. We present a method for leveraging the unlabeled data to learn a better model than could be learned from the labeled data alone. First, a recurrent neural network (RNN) encoder-decoder is trained on the task of predicting nearby turns on the full dialogue corpus; next, the RNN encoder is reused as a feature representation for the supervised learning problem. While previous work has explored the use of pre-training for non-dialogue corpora, our method is specifically geared toward the dialogue use case. We demonstrate an improvement on a clinical documentation task, particularly in the regime of small amounts of labeled data. We compare several types of encoders, both in the context of a classification task and in a human-evaluation of their learned representations. We show that our method significantly improves the classification task in the case where only a small amount of labeled data is available.