Recent progress in deep learning has continuously improved the accuracy of
dialogue response selection. In particular, sophisticated neural network
architectures are leveraged to capture the rich interactions between dialogue
context and response candidates. While remarkably effective, these models also
bring in a steep increase in computational cost. Consequently, such models can
only be used as a re-rank module in practice. In this study, we present a
solution to directly select proper responses from a large corpus or even a
nonparallel corpus that only consists of unpaired sentences, using a dense
retrieval model. To push the limits of dense retrieval, we design an
interaction layer upon the dense retrieval models and apply a set of
tailor-designed learning strategies. Our model shows superiority over strong
baselines on the conventional re-rank evaluation setting, which is remarkable
given its efficiency. To verify the effectiveness of our approach in realistic
scenarios, we also conduct full-rank evaluation, where the target is to select
proper responses from a full candidate pool that may contain millions of
candidates and evaluate them fairly through human annotations. Our proposed
model notably outperforms pipeline baselines that integrate fast recall and
expressive re-rank modules. Human evaluation results show that enlarging the
candidate pool with nonparallel corpora improves response quality further.

该研究提出了一种利用密集检索模型从包含数百万个响应的大型语料库或甚至仅由未配对句子组成的非平行语料库中直接选择适当响应的解决方案，并通过一系列专门设计的学习策略实现其在全范围评估设置下的优越性。