Enhancing Task Bot Engagement with Synthesized Open-Domain Dialog (2023)

Abstract: The construction of dialog systems for various types of conversations, such as task-oriented dialog (TOD) and open-domain dialog (ODD), has been an active area of research. In order to more closely mimic human-like conversations that often involve the fusion of different dialog modes, it is important to develop systems that can effectively handle both TOD and ODD and access different knowledge sources. In this work, we present a new automatic framework to enrich TODs with synthesized ODDs. We also introduce the PivotBot model, which is capable of handling both TOD and ODD modes and can access different knowledge sources to generate informative responses. Evaluation results indicate the superior ability of the proposed model to switch smoothly between TOD and ODD tasks.

## introduction task-oriented dialog (tod) systems and opendomain dialog (odd) systems are two active areas of conversational ai study @xcite . however, most of the existing studies model tod and odd systems separately, leading to a gap between the capabilities of these systems and natural human conversations. in realworld conversations, different dialog modes are often fused, as shown in figure 1 . the conversation may start with casual chats and then move towards task-related requests. along the way, the user may express interest in entities mentioned in the conversation, such as mediterranean food in the given example, leading to a brief odd regarding the entity of interest. the user then returns to task completion, keeping the requests in mind while maintaining a casual conversation. to address the challenge of training dialog models to handle both tod and odd modes, previous research has suggested training models on mixture of tod and odd datasets @xcite or enriching existing tod datasets by combining chitchat with tod system responses @xcite or adding odd to the beginning or end of a tod @xcite . however, these approaches have limitations, including limited information in chitchat augmentation and a lack of explicit distinction between dialog modes. additionally, creating new datasets through human annotation is time-consuming and expensive. while @xcite have introduced a framework for automatically generating dialogs that transition from odd to tod, this method may not be suitable for various mode transitions and cannot simulate informative system utterances with external knowledge. in this work, we introduce a framework to au-tomatically enrich tods with synthesized odds. our approach assumes that users lead conversations with explicit intentions, and that the system's objective is not only to fulfill users' requests but also to generate engaging responses on open-domain topics using external knowledge. we also consider general settings with more flexible dialog mode switches. this paper makes the following contributions: (i) we introduce a general framework for automatically enriching a tod with knowledge-grounded odds and construct the multiwozchat dataset using this framework. (ii) we design a unified model, pivotbot, that performs both tod and odd tasks by predicting the appropriate dialog mode and accessing knowledge sources for response generation. (iii) we show experimental results that demonstrate the effectiveness of pivotbot in conducting seamless conversations of both types. ## proposed framework figure 2 shows the proposed framework for automatically synthesizing one or more knowledgegrounded odds to a given tod. the framework consists of three stages: (1) odd initialization (2) odd simulation, and (3) odd to tod transition. we define the following notations: 1 where n is the number of domains in the dialog, u and s d j i are user and system utterances at turn i in domain j, n i is the number of turns in domain d i , and n is the total number of turns in d. detailed implementation of each module can be found in appendix a. ## conclusion and future work this paper introduces an easily-implemented and generalizable framework for enriching a tod with odds in different settings. a unified model, piv-otbot, with both tod and odd dialog modes is designed. evaluation results demonstrate the effectiveness of the proposed model and the significance of integrating multiple dialog modes for generating appropriate and engaging responses. future work on the data simulation can involve integrating external knowledge, such as knowledge graphs and personality traits, and exploring alternative guided generation methods to improve the consistency and control of the generated odds. to optimize the knowledge retrieval process, train-ing a more efficient retrieval and selection model can be considered. additionally, creating a system with comprehensive capabilities, including recommendation and personalization, would enhance its suitability for real-world applications.