A Zero-Shot Approach for Multi-User Task-Oriented Dialog Generation (2023)

Abstract: Prior art investigating task-oriented dialog and automatic generation of such dialogs have focused on single-user dialogs between a single user and an agent. However, there is limited study on adapting such AI agents to multi-user conversations (involving multiple users and an agent). Multi-user conversations are richer than single-user conversations containing social banter and collaborative decision making. The most significant challenge impeding such studies is the lack of suitable multi-user task-oriented dialogs with annotations of user belief states and system actions. One potential solution is multi-user dialog generation from single-user data. Many single-user dialogs datasets already contain dialog state information (intents, slots), thus making them suitable candidates. In this work, we propose a novel approach for expanding single-user task-oriented dialogs (e.g. MultiWOZ) to multi-user dialogs in a zero-shot setting.

## introduction as ai assistants are increasingly becoming a staple in our social environments like homes, offices and classrooms, it is imperative that these assistants are capable of comprehending conversations and understand requests in social settings. consider the conversations in figure 1 between human users and agents. the first conversation is an example of a single-user conversation, involving a single human user interacting with an ai agent. the second conversation is a conversation between two users that convey the same intent as the first conversation but in a social setting. in contrast with the single-user conversation, the multi-user conversation shows social banter and collaborative decision making arising from the deliberation of a suggestion between the human users, acceptance or rebuttal of suggestions and social banter. these social and conversational cues distract ai agents from tracking the task-relevant information most relevant to them. single-user dialog u: i am looking for a place to dine. the restaurant should serve mediterranean food and should be in the south. a: i'm afraid there aren't any. would you like to try another area or cuisine? u: what restaurants are cheap? generated multi-user dialog u: i am looking for a place to dine. the restaurant should serve mediterranean food and should be in the south. a: i'm afraid there aren't any. would you like to try another area or cuisine? u0: another area? i don't want to go to a place that isn't in the south. it's important to me that the restaurant be in the centre of the city. if you don't have a place in the south, i'll go to a restaurant in the centre. it'll be a new experience for me. u1: maybe you are right. let me see what i can find. there are some places in the centre that i would like to try. if you can't find anything in the centre, then try the south. there are many good restaurants in the south. u0: yes, i would like a restaurant in the south. cheap is fine. figure 1 : an example of a multi-user dialog generated by our approach is shown here along with the reference single-user dialog. the single-user utterance shown in red is expanded as a multi-user dialog (also in red). therefore, it is important to develop ai agents that are robust to multi-user conversations. the development of dialog systems that support multi-user conversations is hindered by the lack of suitable multi-user task-oriented dialog datasets with annotations of user belief states and system actions. collecting such datasets is expensive and it is impractical to support all domains of interest (a typical digital assistant can support thousands of domains). training people to conduct multi-user dialogs is nontrivial, and annotating the dialogs with dialog states is time-consuming. to solve this problem, we propose a zero-shot approach for expanding single-user task-oriented dialogs to automatically generate multi-user taskoriented dialogs (section 3). we do this with two main goals in mind: (1) leverage existing annotations of dialog states in the source single-user dialogs, and (2) ensure a balance between the taskoriented tone and a social tone in the generated multi-user dialogs. to that end, our model expands each user utterance in a single-user dialog to a multi-user dialog that leads to the same user belief state as the source single-user utterance. specifically, our model consists of two dialog generators and a turn planner. a task-oriented generator is trained on single-user task-oriented dialogs (e.g., multiwoz) responsible for generating utterances that contain task-relevant information (e.g. informed or requested slots), and a social generator trained on everyday dialogs (e.g., dailydialog) responsible for generating utterance that express social dynamics of the speakers. we fuse these dialog generators to generate utterances in a multi-user dialog that is conversational, social, and relevant to the task at hand. the turn planner decides on an appropriate mix of dialog generators for generating a particular utterance in multi-user dialog. our automatic and human evaluation (section 4) shows that our approach generates multi-user dialogs that contain social chatter consistent with dialog history and reflect user belief states consistent with source utterances. the main contributions of our work are: ## related work while there are public datasets of task-oriented dialogs annotated with dialog states @xcite @xcite , most of them (if not all) are focused on single-user transactions, i.e., an agent converses with one user at a time. as a result, dialogs in these datasets do not reflect important dynamics of users making decisions together while interacting with an agent. some dialog datasets cover such dynamics, like social banter @xcite and deliberation @xcite ), but they are not task-oriented or annotated with dialog states important for training dialog systems. while the fusedchat data @xcite ) contains taskoriented dialogs with social chatter, the dialogs are still single-user. by contrast, our goal is to build multi-user task-oriented dialogs reflecting social dynamics simultaneously. the most similar line of work to ours is dialog generation from summaries, as a means for data augmentation for dialog summarization. in one framework @xcite , a conversation generator (a seq2seq model) generates a conversation, and a summary generator (a seq2seq model) summarizes the generated conversation. the similarity between the generated summary and the original summary is used as a reward to inform the conversation generator in a reinforcement learning fashion. another approach is to take a seed pair of a dialog and its summary, and gradually modify them iteratively @xcite . specifically, an utterance of the dialog is replaced with a new utterance that is generated based on its context by a seq2seq model. next, the summary is updated based on the updated dialog. data augmentation using these methods improves dialog summarization accuracy in few-shot settings. the main difference between these approaches and ours is that they require seed dialogs. by contrast, we assume the more challenging scenario of zero-shot dialog generation, where we do not have enough dialogs to start with. this setting is more realistic because it is impractical to collect seed conversations of more than 100k domains in case of popular voice assistants. some studies have addressed generation of single-user task-oriented dialogs. one approach is to iteratively generate a user utterance and predict the resulting user belief state using two models @xcite . another approach is to use a variational hierarchical dialog autoencoder that generates dialogs and their underlying dialog states simultaneously @xcite . importantly, these studies still tackle single-user dialogs, ## our approach: multi-user dialog generation our proposed approach automatically generates multi-user task-oriented dialog. the utterances in a multi-user task-oriented dialog can be taskoriented, social or a mix of both. training a single dialog generator capable of handling different kind of dialog turns e.g social or task-oriented requires an aligned dataset. in the absence of such a dataset, we use a bottom up approach of combining dialog generators capable of generating different kind of utterances (e.g social/task-oriented) to generate a multi-user dialog turn-by-turn. the architecture of this approach is shown in figure 2 . we train a task-oriented generator on single-user task-oriented dialogs responsible for generating task-oriented utterance, and a social generator trained on dialogs from everyday scenarios for generating social utterances (section 3.1). we control turn level dynamics by training a turn planner. we use a turn planner (section 3.3) that switches between the two dialog generators utterance-by-utterance to generate a multi-user rewrite. additionally, we propose a mechanism to blend generations from the two dialog generators (section 3.2). ## experiments we compare four variants of our proposed approach for multi-user dialog generations in our evaluations. these variants ablate the effect of (1) late fusion (2) type of turn planner. the variants are described below: