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 pub-lic 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. 1 1 Introduction Until now, researchers have often separated open-domain 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 . or task-oriented dialogues. For example, a lot of prior work focused on building open-domain dialogue systems (Li et al., 2017; Zhang et al., 2018; Adiwardana et al., 2020a), which chat with users via suitable, engaging, safe conversations. With the capability of pre-trained models, a large set of human conversations is adopted to train their capability of free chatting (Zhang et al., 2020; Adiwardana et al., 2020b; Roller et al., 2021). Although these models show the outstanding capability of communicating with human, they are not able to complete tasks as human assistants. On the other hand, MultiWoz (Budzianowski et al., 2018; Hosseini-Asl et al., 2020) and Schema-Guided Dialogue (SGD) (Rastogi et al., 2020) are two popular large-scale datasets of task-oriented dialogues, which include plenty of multi-domain dialogues with state information to track users' behaviors. In task-oriented scenarios, the users have their goals 6143 before starting the conversations, so the way we evaluate the system's performance is whether the system can successfully complete the users' goals. However, both skills of social chatting and task-oriented dialogues are important and may be used in a single conversation. Considering that both skills are essential for a human-like dialogue system, the recent work (Sun et al., 2021) merged those two capabilities by inserting chit-chat sentences into the existing task-oriented dialogue data. The idea is to allow the agent gains more social, personalized communication skills when focusing on task-oriented dialogue generation. Even the released data contains both social and task-oriented dialogues, each dialogue still focuses on a task-oriented scenario where the user has the goal before starting the conversation. In our target scenarios as illustrated in Figure 1, the conversation starts without any specific goal in the user's mind, and the agent explores the potential task-oriented intents and smoothly transitions to a task-oriented conversation. The focus of this paper is more similar to a salesperson's capability, where he/she needs to chat with the user and discovers the implicit task-oriented intents that fit the business purposes and navigates the user to complete a task, such as purchasing a product, reserving a restaurant, or booking a hotel room. Hence, a new pipeline for constructing such data is proposed. Each dialogue in the released dataset starts with discovering a potential task-oriented intent of a user in the social conversation and ends in completing a specific task. Even though high-quality chit-chats and task-oriented dialogues can be separately generated shown in prior work (Hosseini-Asl et al., 2020; Adiwardana et al., 2020b; Roller et al., 2021), how to generate our desired dialogues has not been fully studied and remained unresolved. Yu et al. (2017) built a dialogue framework for users not having a clear intention, where mixing social responses into the conversation guides the flow to a specific movie they want to promote. Our paper has a similar idea about exploring the potential topics in the social conversations and then promoting the targeted tasks. Although the prior work proposed the proper framework for the target scenarios, it required manual rules for dialogue strategies, making it difficult to scale. Also, it only covers a single domain (movie) and there is no any publicly available data for following research work. This paper covers more common topics by taking advantage of the existing natural language generation models trained on substantial dialogue datasets, and releases the first large-scale dialogue dataset with conversations naturally transitioning from chit-chats to task-oriented forms. Our contributions can be summarized as 4-fold:  We present a framework with a simulated user and a simulated salesperson to automatically generate dialogues that smoothly transitions from social chit-chats to task-oriented dialogues, where the components inside the framework can be easily replaced by any desired models for better flexibility. Human evaluation on the generated dialogues demonstrates that the proposed method produces dialogues with reasonable quality and natural conversation flows. We release the first large-scale dataset of dialogues transitioning from chit-chat to task-oriented scenarios, which contains the automatically generated dialogues and the detailed human annotations for enabling the future research work. The released framework with both user and sales simulators allows researchers to generate unlimited dialogues for semi-supervised and unsupervised usage. 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) task-oriented dialogue (TOD) generation. As shown in Figure 1, the conversations start with social chatting between users and salespersons. To generate high-quality open-domain dialogues, the pre-trained dialogue generation models can be adopted. Here we choose BlenderBot (Roller et al., 2021) as our pre-trained generation model due to its outstanding capability trained on the largest-ever open-domain data. It shows the ability to be engaging, knowledgeable, and empathetic at a certain level by multi-tasking on the Blended Skill Talk (BST) dataset (Smith et al., 2020) with several different datasets blending. to discuss in a real-world setting, we manipulate the user and the sales to have different personas in order to cover wide-range topics in our generated dialogues. This can be easily implemented by the package ParlAI 2 (Miller et al., 2017), which allows us to build two BlenderBots to self-chat with each other in order to construct various dialogues involving different personas (Smith et al., 2020). From a salesperson's perspective, how to capture the suitable timing and how to promote the target products/tasks are two main challenges. This paper proposes two components to address the above issues; specifically, a task-oriented intent detector and a transition turn generator focus on capturing the suitable timing and deciding how to smoothly transition to the target task respectively. To find out the good timing during social chatting, we focus on detecting whether the user currently has an implicit intent related to the target tasks. In our case, an intent indicates what a user desires to do or what he/she is very likely to do if someone encourages him/her to do so. If our intent detector is able to capture any task-oriented intent in the social content with diverse topics, it tells us the suitable timing for guiding the dialogue to a specific topic and then transition to a corresponding task-oriented conversation. Table 1 shows the intents we focus on in this paper, and other desired intents can be easily extended by our approach. Although detecting intents in task-oriented dialogues has been studied for long time, the intent detection models trained on task-oriented datasets cannot be directly utilized. The reason is that the in-2 https://parl.ai Intent Description FindMovies find movies to watch GetTimesForMovie obtain the available time for watching a movie FindAttractions find attractions to visit LookupMusic find music to listen to PlaySong play songs LookupSong find songs to listen to Table 1: Descriptions of intents. tents in our scenarios are different from the intents in classical task-oriented data, where former ones are more implicit and the latter ones are more explicit . For example, a user utterance with the intent FindAttraction in our case may be  I never visit France, but I heard that it is a good place.  instead of  Find me the landmarks in Paris.  in classical task-oriented dialogue datasets. Therefore, this paper proposes to leverage the powerful capability of question answering (QA) systems to identify the potential task-oriented intents in a zero-shot fashion (Namazifar et al., 2020). Specifically, we use the pre-trained QA model and ask whether the user has a certain intent given the current dialogue. The questions need to be designed for describing the target task-oriented intents, and we use the following ways to create the questions focusing on task-oriented intents. 3 1. Questions based on descriptions: we create questions associated with all intents based on their natural language descriptions, e.g.  Is the intent asking about playing songs?  for the intent PlaySong . 2. Paraphrased questions: to enhance the detection recall for open-domain dialogues, for each intent, we paraphrase the description-based questions via a high-quality paraphras-3 The manually-designed questions are listed in the Appendix A. 6145 I never visit France, but I heard that it is a good place. The proposed intent detector is illustrated in Figure 3, where the inputs are the open-domain conversation along with intent-related questions, and the outputs are Yes/No answers to these questions. We assume that a user has a task-oriented intent when the detector outputs Yes to the associated question. Note that any type of QA models can be adopted in our framework. Here we start with a QA model pre-trained on large open-domain QA data, e.g., SQuAD (Rajpurkar et al., 2018) or CommonsenseQA (Talmor et al., 2019), which is supposed to be equipped with certain common knowledge and the reasoning ability useful for our intent detector. Furthermore, the general QA model may not be capable of correctly answering intent-related questions since the contexts and questions differ a lot from ones in the general QA data. To reduce the mismatch, we fine-tune the QA model on a publicly available task-oriented dataset (e.g., SGD). Specifically, the annotated intents in task-oriented dialogues are utilized to create the associated QA data, where there is a ground truth answer (Yes/No) to each intent-related question at all dialogue turns. Then the built training data (TOD-QA shown in Figure 3) allows the general QA model to better identify task-oriented intents. Although fine-tuned on the task-oriented dataset, we find that the model benefits from pre-training and thus it can be well applied to open-domain dialogues. This section describes how we generate the transition turn that bridges open-domain and task-oriented dialogues. Our transition turn generation procedure is composed of two parts: 1) using a template transition sentence to trigger the corresponding task-oriented user reaction and 2) re-generating the transition turn for better fluency and diversity. Template-based For each task-oriented intent, we adapt its intent description in the ontology to create a corresponding template question (e.g., Do you want to [Intent Description]? ) as the transition sentence shown in the upper block of Figure 4. Although using template-based transition is simple and effective, it however makes the salesperson too aggressive and invariant to be professional. Generative-based To improve the fluency of transition and increase the diversity of word usage, we propose a generative-based approach to re-generate more smooth and nature transitions. With a similar idea as (Ennen et al., 2021; Sevegnani et al., 2021), our goal is to predict a transition utterance that can naturally bridge the past and the future utterances as below. Specifically, we feed the last user's open-domain utterance and the first user's task-oriented utterance in our generated data as inputs, and learn to predict the template transition turn. To learn the capability of connecting different topics smoothly, the newly published data OTTers (Sevegnani et al., 2021) is leveraged for training our generative model. This data focuses on bridging two different topics via the transition in an entity path of a commonsense knowledge graph. The assumption of using this dataset is that open-domain utterances can be viewed as the previous topic and task-oriented utterances as the new one, so learning the transition turn 6146 is the same as learning how to smoothly transition from open-domain to task-oriented dialogues. After detecting the potential task-oriented intent and generating the transition turn, it is natural to continue the dialogue in a task-oriented scenario illustrated in the right part of Figure 2. Here we propose two ways of generating task-oriented dialogues following the transition turn. Merge SGD It is naive to simply merge an appropriate task-oriented dialogue taken from TOD data with a chit-chat dialogue to create such dialogue. In more details, all task-oriented dialogues in the SGD dataset are grouped by intents, and one TOD dialogue is sampled based on the detected task-oriented intent to append to the transition turn and form a new dialogue containing both chit-chat and TOD. Note that the delexical-ized version of SGD (Sun et al., 2021) is used to avoid severe inconsistency between open-domain and task-oriented parts. Task-Oriented Simulation Different from open-domain social chatting, the roles in task-oriented dialogues are important. Therefore, two task-oriented simulators are trained, one for users and another for salespersons. Considering that training on task-oriented dialogues from scratch may limit the diversity of the generated dialogues, to generate the context-aware, fluent, and consistent conversations, we use the same type of open-domain dialogue generation models, BlenderBot (Roller et al., 2021), and additionally train on either user turns or agent turns in task-oriented dialogues for TOD User BlenderBot and TOD Sales BlenderBot. By allowing two simulators to talk with each other, they can generate endless conversations until one of the termination conditions is satisfied. There are three commonly used termination strategies we use when building our dataset: (1) Any pre-defined keyword appears in the utterance, e.g., bye . (2) The sales simulator generates a special token representing the ending of a dialogue. (3) When the dialogue starts to repeat itself, i.e., repeatedly producing the same utterances, because it usually means no more useful information. The proposed framework enables us to construct a large-scale dataset with dialogues transitioning from open-domain to task-oriented scenarios, which align well with the salesperson's business potential. We use a widely-used crowdsourcing platform, Amazon Mechanical Turk (AMT) 4 , to collect human feedback for our generated dialogues. Intent Detector Our QA model is DistillBert (Sanh et al., 2020) pre-trained on the general QA data, SQuAD 2.0 (Rajpurkar et al., 2018), and then fine-tuned on TOD data, SGD. The value of learning rate and batch size are 3e-5 and 64 respectively with AdamW optimizer (Loshchilov and Hutter, 2019) for 20 epochs. Transition The T5 (T5-small) model is trained to generate transitions with a learning rate of 5e-5 with Adafactor optimizer (Shazeer and Stern, 2018) and batch size of 16. We train our models for 5 epochs and select the model with lowest loss in the dev set. During decoding, we mix top-K sampling of 80 and top-p (nucleus) sampling of 0.95 (Holtzman et al., 2020). Dialogue Generation To generate task-oriented utterances, we train our two simulators on the model BlenderBot-400M 5 with the same training setting with generative transition. During generation, we use top-K sampling (Fan et al., 2018), where K is set to 120 for encouraging more diversity in dialogues. We randomly pick about 4,000 dialogues for two human evaluation tasks submit to AMT. The first task is designed for collecting feedback about the entire dialogue, while the second one focuses on the transition part due to the main goal of this paper. Table 2 shows the dialogue example presented to crowdworkers for evaluation. Because our target dialogues can be viewed as salespersons' capability, we describe the context to the recruited crowdworkers that the given dialogues are between a beginner salesperson and his/her customer and ask workers to provide feedback from different aspects in terms of the sales' strategies. Note that the annotators are not aware that the dialogues are machine-generated, so the collected feedback is for human sales communication skills. Each task is briefly described below, and the full annotation guideline can be found in the Appendix B. 4 https://www.mturk.com/ 5 https://parl.ai/projects/blenderbot2/ 6147 Sales: Cigarettes are pretty bad for you, but they're so tasty! Do you like to smoke in your free time? User: Yes, I enjoy smoking in my free time. I also enjoy lifting weights and squatting. Sales: That's great! I like to lift weights as well. It's a great way to stay in shape. User: I love to play video games. I play a lot of League of Legends. What about you? Sales: Yes, it is. I enjoy it a lot. What else do you enjoy doing in your spare time? User: I enjoy playing video games as well! I also like to go to the movies. Sales: I usually watch a movie. I found [count] movies. What about [title], [title], or [title]? [Transi-tion] User: I wanna find some Romance movies online. Sales: What do you think about [title], [title] or [title]? User: Is there anything else? Violent movies would be nice. I like the director Jia Zhangke. Sales: There's [count] movie that might suit your interests. What about [title]? User: Ash Is Purest White sounds great. I wanna watch that movie now. I wanna watch it with subtitles. Task 2: Chit-Chat to Task-Oriented Transition In addition to the entire dialogue, we also explicitly highlight the transition turn in the dialogue when presenting to crowdworkers. Similarly to the first task but only focusing on the transition part, we asked workers to rate from 1 to 5 from the following aspects: Right Time (Q1Is it a good timing to make the transition?), Relevance (Q2Is the transition relevant to the conversation context?), Aggressiveness (Q3Is the transition aggressive?), and Overall (Q4Do you think it is overall a good transition?). In each question, the detailed descriptions of all ratings are given to crowdworkers to ensure they have consistent understanding for all ratings. In addition, to enrich the transition turns and ensure their quality, we generate 4 additional transitions and ask workers to choose the best one. All transitions and ratings are included in our released data. Task 3: Customer's Implicit Intent Considering that detecting potential intents plays an important role in our framework, we further investigate the influence of intent detectors. To evaluate the performance of different detectors, crowdworkers are presented with a conversation snippet and the detected intent results from three detectors, and they are asked to rank the intents in terms of their relevance to the conversation. Three evaluated detectors are: Detector1 pre-trained on SQuAD 2.0 (Section 3.1), Detector2 additionally pre-trained on SWAG (Zellers et al., 2018) and CommonsenseQA (Talmor et al., 2019), and Detector3  adapted from TransferQA (Lin et al., 2021), which learns dialogue state tracking knowledge from several general QA datasets. We evaluate 1,500 conversation snippets, and three workers are recruited to rank intents for each snippet. 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. Table 3 presents the statistics of the randomly sampled dialogues submitted to AMT. The average length of chit-chat turns in Merge SGD and TOD Simulation are about 4.5. The evaluation results of all dialogues are visualized in the top charts of Figure 5, and the bottom charts show the results for existing TOD data (Merge) and simulator-generated TOD (Simulator). It can be observed that our framework is able to produce context-relevant task-oriented conversations to match the topic of open-domain dialogues (Q1 in T1; Q2 in T2). This indicates that we can ensure the dialogue flow from open-domain to task-oriented dialogues is natural. The median relevance scores are slightly higher than the Neutral line, sug-6148 Q1:Relevant Q2:Aggressive Q3:Overall 1 2 3 4 5 S c o r e Q1:Timing Q2:Relevant Q3:Aggressive Q4:Overall 1 2 3 4 5 S c o r e Q1:Relevant Q2:Aggressive Q3:Overall 1 2 3 4 5 S c o r e Merge Simulator Task 1: Conversation Evaluation Q1:Timing Q2:Relevant Q3:Aggressive Q4:Overall 1 2 3 4 5 S c o r e Merge Simulator Task 2: Transition Evaluation Figure 5: Score distribution of task 1 (left) and 2 (right). gesting that our sales simulator can perform his sales strategy without annoying customers. The observation further demonstrates the feasibility and effectiveness of our proposed method. In terms of the salesperson's aggressiveness, crowdworkers think that the transition is neutral and somewhat aggressive, showing that smoothly transitioning is still an important research problem to explore. Furthermore, the transition timing scores (Q1 in T2) also demonstrate that our proposed task-oriented intent detection can capture a suitable moment in a zero-shot setting, so that the sales may not miss any business opportunity of product promotion. We can observe that most of overall scores (Q3 in T1; Q4 in T2) are above Neutral (Score 3) 6 , indicating that the generated dialogues and transitions are overall good for a salesperson's business perspective. The human judgement demonstrates that our proposed approach is capable of simulating a large-scale reasonable dialogues aligned with our 6 The full description of each score is presented in Appendix B. purpose, implying that both research community and industries can greatly benefit from our released data and the built simulators that can continuously generate more data for training. Our framework and the constructed dataset reduce the cost for large-scale data requirement for better practice. To further investigate whether the proposed TOD simulators described in Section 2.3 can generate reasonable dialogues compared to Merge SGD , we visualize their individual scores as shown at the bottom of Figure 5. There is no significant difference between two groups, and we further investigate their score distribution of each question shown in Figure 6. Both results tell that given the context of open-domain utterances, our TOD simulators are able to generate the suitable task-oriented dialogues with comparable quality to those from the publicly available benchmark TOD dataSGD. Consequently, our framework can be utilized to generate large-scale data cost-effectively and the generation quality is comparable with the current benchmark dialogue data. Table 4 shows the average ranks of three detectors described in T3. We find that Detector1 (pre-trained on SQuAD 2.0) and Detector2 (pre-trained on SQuad 2.0, SWAG, CommonsenseQA) perform almost the same, implying that simply pretraining on extra commonsense-related QA data may not significantly improve the ability of detecting implicit intents. Possible reasons may be either that these datasets include quite similar knowledge about our target intents, or our zero-shot QA model reaches its capacity bottleneck. How to better utilize commonsense knowledge for detecting potential intents can be further investigated in the future. Lin et al. (2021) has demonstrated Detector3 (trained on several QA datasets) is able to achieve decent dialogue state tracking performance in zero-shot settings. Therefore, we did not fine-tune it on the task-oriented datasets such as SGD Detector1 and Detector2 are fine-tuned on. However, according to its average rank, Detector3 is significantly worse than other detectors. Probably because the intents in chit-chat conversations are more implicit and complex than task-oriented intents, the ability of detecting implicit intents cannot be easily transferred. In addition to the proposed framework and the released dataset, our collected human judgement has the potential of providing valuable contributions to dialogue community and industrial products. Each question along with its corresponding scores can be treated as a interested task, and we briefly describe some (but not limited to) examples of crowd-sourced data usage. The human scores from T1 can be formulated as classification or regression annotations which measure the relevance between a recommended product and a conversation context, whether a salesperson in a dialogue is too aggressive, or the overall quality of a sales dialogue. Similarly, we can apply these ideas to T2, which focuses on evaluating transitions. Particularly, deciding when is a good to perform a transition can be an interesting topic for future research. This will also benefit industries to develop more intelligent dialogue systems interacting with customers. Moreover, the rank annotations provided by workers from T3 can be considered as high-quality data for training a ranking model or an intent detector. Apart from this, the data can also be utilized as a gold standard to assess the performance of different algorithms predicting user implicit intents. We expect these examples will inspire the community and industries to discover more interesting research directions and applications. Our work is related to dataset construction for building persuasive dialogue systems that try to persuade the participant to take a specific action. Hiraoka et al. (2014) annotated 34 dialogues, in which an experienced salesperson tries to convince a customer to buy a camera. Yoshino et al. (2018) 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. Wang et al. (2019) 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 6150 conversational recommendation systems. These studies (Wu et al., 2019; Zhou et al., 2020; Xu et al., 2020) 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, Liu et al. (2020) 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. Tang et al. (2019) 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. Sevegnani et al. (2021) 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. Sun et al. (2021) enhanced the utterances in task-oriented dialogues by appending chit-chat sentences. They hope that the agent gains more social, personalized, and engaging communication skills. Ennen et al. (2021) 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 Yu et al. (2017) and Young et al. (2022). Yu et al. (2017) 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, Young et al. (2022) 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. This paper proposes a novel framework to generate dialogues that naturally transition from open-domain 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. We thank reviewers for their insightful comments. This work was financially supported from MediaTek Research, Amazon AWS Machine Learning Research Awards, and the Young Scholar Fellowship Program by Ministry of Science and Technology (MOST) in Taiwan, under Grant 111-2628-E-002-016.
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