U ni DS : A Unified Dialogue System for Chit-Chat and Task-oriented Dialogues (2022)

Abstract: With the advances in deep learning, tremendous progress has been made with chit-chat dialogue systems and task-oriented dialogue systems. However, these two systems are often tackled separately in current methods. To achieve more natural interaction with humans, dialogue systems need to be capable of both chatting and accomplishing tasks. To this end, we propose a unified dialogue system (UniDS) with the two aforementioned skills. In particular, we design a unified dialogue data schema, compatible for both chit-chat and task-oriented dialogues. Besides, we propose a two-stage training method to train UniDS based on the unified dialogue data schema. UniDS does not need to adding extra parameters to existing chit-chat dialogue systems. Experimental results demonstrate that the proposed UniDS works comparably well as the state-of-the-art chit-chat dialogue systems and task-oriented dialogue systems. More importantly, UniDS achieves better robustness than pure dialogue systems and satisfactory switch ability between two types of dialogues.

## introduction dialogue system is an important tool to achieve intelligent user interaction, and it is actively studied by nlp and other communities. current research of dialogue systems focus on task-oriented dialogue (tod) systems @xcite @xcite , achieving functional goals, and chit-chat dialogue systems aiming at entertainment @xcite @xcite . different methods are devised for these two types of dialogue systems separately. however, a more suitable way for users would be to have one dialogue agent that is able to handle both chit-chat and tod in one conversation. as illustrated in figure 1 , users may have communication-oriented needs (e.g. chatting about money and happiness) and task-oriented needs (e.g. hotel reservation) when interacting with a dialogue agent. furthermore, inputs of dialogue systems are often interfered by background noise, such as voice from other people or devices, collected by the preceding automatic speech recognition (asr) module. therefore, the chit-chat ability may also improve the robustness of a task-oriented dialog system @xcite . as shown in table1, there are many differences between chit-chat and task-oriented dialogues. creating a single model for different tasks without performance degradation is challenging @xcite . some works attempt to model different dialogue skills via different experts or adapters @xcite . however, these methods increase the number of parameters and hard to achieve satisfactory performance on both types of dialogues. besides, previous work lack the exploration of the ability to switch between different types of dialogues. ## related work with the development of large-scale language models, chit-chat dialogue systems achieve remarkable success. based on gpt-2 @xcite , dialogpt @xcite ) is further trained on large-scale dialogues extracted from reddit. di-alogpt could generate more relevant, contentful, and fluent responses than previous methods. afterwards, larger pre-train lm based chit-chat dialogue systems @xcite @xcite are proposed and achieve even better performance. in the area of task-oriented dialogue systems, recent research @xcite @xcite concatenated elements in a dialogue into one sequence and utilized pre-train lm to generate the belief state, system act, and response in an end-to-end way and achieved promising results. there are several works related to the unified dialogue system. @xcite insert one turn chit-chat dialogue into task-oriented dialogues to train a model with better out-of-domain recovery ability. attention over parameters (aop) @xcite utilizes different decoders for different dialogue skills (e.g., hotel booking, restaurant booking, chit). however, the performance of aop can be improved and it largely increases parameters comparing with models that handle a single type of dialogues. accentor @xcite adds chit-chat utterance at the beginning or end of task-oriented responses to make the conversation more engaging, but accentor is unable to have a chit-chat with users. unlike the above works, unids does not add extra parameters to existing dialogue models, and unids could alternatively handle chit-chat and task-oriented dialogues in a seamless way. ## conclusion this paper proposes a unified dialogue system (unids) to jointly handle both chit-chat and taskoriented dialogues in an end-to-end framework. specifically, we propose a unified dialogue data schema for both chit-chat and task-oriented dialogues, and a two-stage method to train unids. to our best knowledge, this is the first study towards an end-to-end unified dialogue system. experiments show that unids performs comparably with state-of-the-art chit-chat dialogue systems and task-oriented dialogue systems without adding extra parameters to current chit-chat dialogue systems. more importantly, the proposed unids achieves good switch ability and shows better robustness than pure task-oriented dialogue systems. although question answering (qa) is not considered in the proposed unids, as an initial attempt, our explorations may inspire future studies towards building a general dialogue system.