  We study learning of a matching model for response selection in
retrieval-based dialogue systems. The problem is equally important with
designing the architecture of a model, but is less explored in existing
literature. To learn a robust matching model from noisy training data, we
propose a general co-teaching framework with three specific teaching strategies
that cover both teaching with loss functions and teaching with data curriculum.
Under the framework, we simultaneously learn two matching models with
independent training sets. In each iteration, one model transfers the knowledge
learned from its training set to the other model, and at the same time receives
the guide from the other model on how to overcome noise in training. Through
being both a teacher and a student, the two models learn from each other and
get improved together. Evaluation results on two public data sets indicate that
the proposed learning approach can generally and significantly improve the
performance of existing matching models.
