Response selection plays a vital role in building retrieval-based
conversation systems. Despite that response selection is naturally a
learning-to-rank problem, most prior works take a point-wise view and train
binary classifiers for this task: each response candidate is labeled either
relevant (one) or irrelevant (zero). On the one hand, this formalization can be
sub-optimal due to its ignorance of the diversity of response quality. On the
other hand, annotating grayscale data for learning-to-rank can be prohibitively
expensive and challenging. In this work, we show that grayscale data can be
automatically constructed without human effort. Our method employs
off-the-shelf response retrieval models and response generation models as
automatic grayscale data generators. With the constructed grayscale data, we
propose multi-level ranking objectives for training, which can (1) teach a
matching model to capture more fine-grained context-response relevance
difference and (2) reduce the train-test discrepancy in terms of distractor
strength. Our method is simple, effective, and universal. Experiments on three
benchmark datasets and four state-of-the-art matching models show that the
proposed approach brings significant and consistent performance improvements.

本文提出一种利用现成的响应检索模型和响应生成模型来自动生成灰度数据并利用多级排序目标进行训练的方法，从而在建立基于检索的对话系统中提高响应选择的性能。实验表明，该方法能显著且一致地提高基于三个基准数据集和四种最先进的匹配模型的性能。