S ales B ot: Transitioning from Chit-Chat to Task-Oriented Dialogues (2022)

Abstract: Dialogue systems are usually categorized into two types, open-domain and task-oriented. The first one focuses on chatting with users and making them engage in the conversations, where selecting a proper topic to fit the dialogue context is essential for a successful dialogue. The other one focuses on a specific task instead of casual talks, e.g., finding a movie on Friday night, playing a song. These two directions have been studied separately due to their different purposes. However, how to smoothly transition from social chatting to task-oriented dialogues is important for triggering the business opportunities, and there is no any public data focusing on such scenarios. Hence, this paper focuses on investigating the conversations starting from open-domain social chatting and then gradually transitioning to task-oriented purposes, and releases a large-scale dataset with detailed annotations for encouraging this research direction. To achieve this goal, this paper proposes a framework to automatically generate many dialogues without human involvement, in which any powerful open-domain dialogue generation model can be easily leveraged. The human evaluation shows that our generated dialogue data has a natural flow at a reasonable quality, showing that our released data has a great potential of guiding future research directions and commercial activities. Furthermore, the released models allow researchers to automatically generate unlimited dialogues in the target scenarios, which can greatly benefit semi-supervised and unsupervised approaches.

## introduction until now, researchers have often separated opendomain and task-oriented dialogues as two distinct types of tasks in the dialogue field. the publicly available datasets focuses on either open-domain 1 our dataset, trained simulators, and annotations are available at: https://github.com/miulab/salesbot . ## proposed approach figure 2 illustrates our proposed framework for constructing the dataset. it can be divided into three main parts: (1) open-domain dialogue generation, (2) chit-chat to task-oriented transition, and (3) taskoriented dialogue (tod) generation. ## data quality evaluation we use a widely-used crowdsourcing platform, amazon mechanical turk (amt) foot_1 , to collect human feedback for our generated dialogues. ## results and analysis for brevity, we use t to denote task in the following. each dialogue is evaluated by three crowdworkers so that we can check the annotation variance for reliable results. ## related work our work is related to dataset construction for building persuasive dialogue systems that try to persuade the participant to take a specific action. @xcite annotated 34 dialogues, in which an experienced salesperson tries to convince a customer to buy a camera. @xcite requested crowdsourcing workers to generate 200 persuasive dialogues. in each dialogue, one participant persuaded another one to adopt his suggestion such as cleaning a room. @xcite collected 1017 dialogues, in which one of the participants was convinced to donate to a specific charity. we can see that the covered conversation scenarios in these datasets were strictly limited to specific tasks, while our scenarios are more general and can be easily extended to different cases. also, our constructed dataset is about three times larger than the prior work, indicating the usefulness of the recent pre-trained paradigm. the topic of conversational recommendation systems is also related to our work. a number of attempts have been made to collect training data for conversational recommendation systems. these studies @xcite @xcite first extracted a path consisting of an entity or attribute nodes from a knowledge base. then they asked annotators to write conversational recommendation dialogues. the flow of mentioned topics in a dialogue should follow the extracted path. similarly, @xcite also built a dataset by asking human workers to create dialogues based on a topic path. it should be noted that, in these datasets, the goal of such systems is to only make entity recommendations instead of tasks, while our work goes beyond them in naturally transferring from chit-chat to task-oriented dialogues and completing a task the user may want. another related work is generating a transition between two given open-domain utterances. @xcite proposed to generate the transition conditional on a specific word, because they want the generated transition can drive the conversation topic to the specified word. @xcite collected a new dataset of human-created one-turn topic transitions. each dialogue contains 2 utterances with different topics and 1 transition in the middle of them. there are some recent studies trying to merge chit-chat and task-oriented dialogues, but the purposes of merged dialogues differ from ours. @xcite enhanced the utterances in taskoriented dialogues by appending chit-chat sentences. they hope that the agent gains more social, personalized, and engaging communication skills. @xcite proposed a dialogue system that can transfer the style of generated response from chit-chat to task-oriented styles. however, the system is a prototype model, there is still a large gap to properly bridge chitchat and task-oriented dialogues. the motivation of our work is closely similar to the studies by @xcite and @xcite . @xcite manually created several task-oriented response generation strategies specifically designed for the movie promotion scenario. in addition, the expert knowledge was utilized to design reinforcement learning rewards that help their dialogue system to decide which action to take (i.e., continuing chit-chat or selecting a task-oriented strategy to reply). in order to fuse open-domain and task-oriented dialogues to a complete and natural conversation, @xcite manually rewrote existing task-oriented utterances and added new open-domain conversations. the most crucial difference between their work and ours is that, in their dialogues, the user "explicitly" expressed his/her intentions indicating clear clues about when and how to naturally transit from chitchat to task-oriented conversations, while our user intentions are "implicit" which makes detection and transition more challenging. however, we also observe that the prior work in these studies heavily relied on human efforts (data collection, expert-created strategies, etc.). therefore, it can be expensive and hard to extend their data or method the practical cases due to the requirement of larger-scale training data. our proposed framework benefits from the pre-trained models and shows its outstanding conversational capability. the flexibility of extending to diverse cases is also validated, considering that all components inside the framework can be easily substituted by the updated models, and the generated data can be used by semi-supervised or unsupervised methods for cold-start scenarios. ## conclusion this paper proposes a novel framework to generate dialogues that naturally transition from opendomain to task-oriented scenarios at a large scale without heavy human efforts. our proposed chitchat to task-oriented transition approach can capture the suitable timing when the user shows the implicit intents and generate the diverse and natural transition turn to trigger the task-oriented utterances. our human evaluation shows that the automatically generated dialogues have a reasonable quality with natural conversation flows from a business point of view. the released dataset and framework empowers research community to easily obtain large-scale target dialogues and the human annotated scores can be utilized for related work. this paper has a great potential of guiding future research directions and benefiting the community of both research and industry.