Dialogue Distillation: Open-Domain Dialogue Augmentation Using Unpaired Data

• DOI: 10.18653/v1/2020.emnlp-main.277
• Publication Date: 2020-11-29T14:51:46Z
• Authors: Rongsheng Zhang; Yinhe Zheng; Jianzhi Shao; Xiaoxi Mao; Yadong Xi; Minlie Huang
• Keywords: Benchmark (surveying)

Recent advances in open-domain dialogue systems rely on the success of neural models that are trained large-scale data. However, collecting data is usually time-consuming and labor-intensive. To address this dilemma, we propose a novel augmentation method for training by utilizing unpaired Specifically, data-level distillation process first proposed to construct augmented dialogues where both post response retrieved from A ranking module employed filter out low-quality dialogues. Further, model-level distill teacher model high-quality paired pairs, thereby preventing being affected noise Automatic manual evaluation indicates our can produce pairs with diverse contents, improve performance competitive baselines.