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.

研究使用一个匹配模型来解决对话系统的响应选择问题，并提出一个包含三种特定教学策略的通用共同教学框架，该框架可以从含噪声的训练数据中学习出一个强健的匹配模型，以及通过在独立训练数据中同时学习两个匹配模型，从而达到显着提高匹配模型性能的目的。

基于Co-teaching 的多回合响应选择检索对话系统匹配模型学习