MPFToD: a modularized pre-training framework for consistency identification in task-oriented dialogue

Libo Qin, Shijue Huang, Qiguang Chen, Qian Liu, Wanxiang Che, Ruifeng Xu

Abstract: Consistency identification in task-oriented dialogue (CI-ToD) can prevent inconsistent dialogue response generation, which has recently emerged as an important and growing research area. This paper takes the first step to explore a pre-training paradigm for CI-ToD. Nevertheless, pre-training for CI-ToD is non-trivial because it requires a large amount of multi-turn KB-grounded dialogues, which are extremely hard to collect. To alleviate the data scarcity problem for pre-training, we introduce a modularized pre-training framework (MPFToD), which is capable of utilizing large amounts of KB-free dialogues. Specifically, such modularization allows us to decouple CI-ToD into three sub-modules and propose three pre-training tasks including (i) query response matching pre-training; (ii) dialogue history consistent identification pre-training; and (iii) KB mask language modeling to enhance different abilities of CI-ToD model. As different sub-tasks are solved separately, MPFToD can learn from large amounts of KB-free dialogues for different modules, which are much easier to obtain. Results on the CI-ToD benchmark show that MPFToD pushes the state-of-the-art performance from 56.3% to 61.0%. Furthermore, we show its transferability with promising performance on other downstream tasks (i.e., dialog act recognition, sentiment classification and table fact checking).