Domain adaptation has recently become a key problem in dialogue systems
research. Deep learning, while being the preferred technique for modeling such
systems, works best given massive training data. However, in the real-world
scenario, such resources aren't available for every new domain, so the ability
to train with a few dialogue examples can be considered essential. Pre-training
on large data sources and adapting to the target data has become the standard
method for few-shot problems within the deep learning framework. In this paper,
we present the winning entry at the fast domain adaptation task of DSTC8, a
hybrid generative-retrieval model based on GPT-2 fine-tuned to the multi-domain
MetaLWOz dataset. Robust and diverse in response generation, our model uses
retrieval logic as a fallback, being SoTA on MetaLWOz in human evaluation (>4%
improvement over the 2nd place system) and attaining competitive generalization
performance in adaptation to the unseen MultiWOZ dataset.

本文提出了基于 GPT-2 微调多领域 MetaLWOz 数据集的混合生成 - 检索模型，具有稳健、多样的响应生成能力，在领域自适应任务中取得了最优结果，并在适应未见过的 MultiWOZ 数据集时表现出竞争力的泛化性能。