Learning a Matching Model with Co-teaching for Multi-turn Response
Selection in Retrieval-based Dialogue Systems
Abstract
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