Implicitly-Supervised Learning In Spoken Language Interfaces: An Application To The Confidence Annotation Problem - Bohus, Dan; Rudnicky, Alexander (Proceedings of SIGdial 2007, Antwerp, Belgium, 2007)
In this paper we propose the use of a novel learning paradigm in spoken language interfaces – implicitly-supervised learning. The central idea is to extract a supervision signal online, directly from the user, from certain patterns that occur naturally in the conversation. The approach eliminates the need for developer supervision and facilitates online learning and adaptation. As a first step towards better understanding its properties, advantages and limitations, we have applied the proposed approach to the problem of confidence annotation. Experimental results indicate that we can attain performance similar to that of a fully supervised model, without any manual labeling. In effect, the system learns from its own experiences with the users. Subsequent experiments with the machine learning infrastructure used in this work have revealed a small defect in the model construction and evaluation. During the stepwise model building process, the scoring of features was done by assessing performance on the entire dataset (including train + development folds), instead of exclusively on the train folds. Nevertheless, once a feature to be added to a model was selected, the model was trained exclusively on the training folds, i.e. the corresponding feature weight in the max-ent model was determined based only on the training data, and the evaluation was done on the held-out development fold. Subsequent experiments with a correct setup (where the feature scoring is done only by looking at the training folds) on several problems show that this bug does not significantly affect results. While with a correct setup the numbers reported might differ by small amounts, we believe the general results we have reported in this paper stand.