Multi-Task Pre-Training for Plug-and-Play Task-Oriented Dialogue System (2022)

Abstract: Pre-trained language models have been recently shown to benefit task-oriented dialogue (TOD) systems. Despite their success, existing methods often formulate this task as a cascaded generation problem which can lead to error accumulation across different sub-tasks and greater data annotation overhead. In this study, we present PPTOD, a unified plug-and-play model for task-oriented dialogue. In addition, we introduce a new dialogue multi-task pre-training strategy that allows the model to learn the primary TOD task completion skills from heterogeneous dialog corpora. We extensively test our model on three benchmark TOD tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. Experimental results show that PPTOD achieves new state of the art on all evaluated tasks in both high-resource and low-resource scenarios. Furthermore, comparisons against previous SOTA methods show that the responses generated by PPTOD are more factually correct and semantically coherent as judged by human annotators.

## introduction task-oriented dialogue is often decomposed into three sub-tasks: (1) dialogue state tracking (dst) for tracking user's belief state; (2) dialogue policy learning (pol) for deciding which system action to take; (3) natural language generation (nlg) for generating dialogue response @xcite . traditional approaches @xcite adopt a modularized pipeline that addresses different sub-tasks with distinct dedicated modules. in contrast, recent systems @xcite @xcite integrate all functionalities required to hold a dialogue into neural network models. with the advances in pre-trained language models (plms) @xcite @xcite , different systems based on plms have been proposed @xcite @xcite . despite their differences, most existing methods formulate task-oriented dialogue as a cascaded generation problem, that is, the model can only solve latter sub-tasks by conditioning on the outputs of previous ones. for instance, to generate the response (nlg), the model must rely on the outputs of previous sub-tasks (i.e., @xcite . while impressive results are reported @xcite , we identify three major limitations in the cascaded formulation of their system design. (1) firstly, as the model solves all sub-tasks in a sequential order, the errors accumulated from previous steps are propagated to latter steps @xcite . (2) secondly, the training data must be annotated for all sub-tasks. such annotation requirement significantly increases the data curation overhead. more importantly, it precludes the model from using the large amount of existing data that is partially annotated (e.g., data only annotated with dst or nlg). in this study, we propose a novel plug-and-play task-oriented dialogue (pptod) system. figure 1 depicts an illustration of our approach. as seen, we integrate different dialogue modules (e.g. dst, pol, and nlg) into a unified model. motivated by the concept of in-context learning @xcite , to steer the model to solve different tod sub-task, we plug a task-specific natural language instruction, termed as prompt, into the dialogue context as the model input. this way, the generations of different sub-tasks are decoupled, leading to a greater flexibility of the model that brings us at least two advantages: (1) as different sub-tasks are figure 1 : overview: in the dialogue multi-task pre-training stage, we pre-train our model with four tod-related tasks, including natural language understanding (nlu), dialogue state tracking (dst), dialogue policy learning (pol), and natural language generation (nlg). for each task, the model takes the dialogue context and the taskspecific prompt as input and learns to generate the corresponding target text. our learning framework allows us to train the model with partially annotated data across a diverse set of tasks. (best viewed in color) inspired by recent success of dialogue language model pre-training @xcite @xcite , we propose a dialogue multi-task pre-training strategy that equips our model with the primary tod task completion skills. specifically, initialized with t5 @xcite , we pre-train our model on a heterogeneous set of dialog corpora that consist of partially-annotated data. to build the pre-training corpora, we collect and combine eleven human-written multi-turn dialogue corpora. the collected datasets are partially annotated for some of the tod-related tasks, including natural language understanding (nlu), dialogue state tracking (dst), dialogue policy learning (pol), and natural language generation (nlg). in total, the pre-training corpora contain over 2.3m utterances across over 80 domains (see more details in we evaluate pptod on a wide range of benchmark tod tasks, including end-to-end dialogue modelling, dialogue state tracking, and intent classification. comparisons against previous state-of-theart approaches show that pptod achieves better performance in both full-training and low-resource settings as judged by automatic and human evaluations. in summary, our contributions are: ## methodology in this section, we first discuss the datasets and learning objective used in the proposed dialogue multi-task pre-training. then we introduce how to apply the pre-trained pptod for a new task. ## experiments we test pptod on three benchmark tod tasks: ## further analysis in this section, we present further discussions and empirical analyses of the proposed model. ## conclusion in this paper, we propose pptod, a unified model that supports both task-oriented dialogue understanding and response generation in a plug-andplay manner. in addition, we introduce a new dialogue multi-task pre-training strategy to further augment our model's ability in completing todrelated tasks. extensive experiments and analysis are conducted on three benchmark tod tasks in both high-resource and low-resource settings. the automatic and human evaluations demonstrate that pptod outperforms the current sota systems in terms of various evaluation metrics.