Building a Role Specified Open-Domain Dialogue System Leveraging Large-Scale Language Models (2022)

Abstract: Recent open-domain dialogue models have brought numerous breakthroughs. However, building a chat system is not scalable since it often requires a considerable volume of human-human dialogue data, especially when enforcing features such as persona, style, or safety. In this work, we study the challenge of imposing roles on open-domain dialogue systems, with the goal of making the systems maintain consistent roles while conversing naturally with humans. To accomplish this, the system must satisfy a role specification that includes certain conditions on the stated features as well as a system policy on whether or not certain types of utterances are allowed. For this, we propose an efficient data collection framework leveraging in-context few-shot learning of large-scale language models for building role-satisfying dialogue dataset from scratch. We then compare various architectures for open-domain dialogue systems in terms of meeting role specifications while maintaining conversational abilities. Automatic and human evaluations show that our models return few out-of-bounds utterances, keeping competitive performance on general metrics. We release a Korean dialogue dataset we built for further research.

## introduction recent large-scale language models (lms) have brought numerous breakthroughs in open-domain dialogue systems, yielding human-like responses @xcite @xcite @xcite . in addition, there have been progresses in controlling dialogue systems in persona, style, and safety @xcite @xcite , which impose consistency on chatbot's personality and mitigate undesirable features 1 the dataset is available at https://github.com/ naver-ai/carecall-corpus figure 1 : an example of a chatbot system that cares for senior citizens living alone. the utterance in red highlights the model's mistaken identity as a chef rather than the caring chatbot. most task-oriented dialogue systems conduct specific roles such as booking assistants, information providers, customer service agents, or personal assistants @xcite @xcite . however, studies on open-domain dialogue systems that perform specific roles have been insufficiently investigated, even though the role can be defined for the practical chatbot systems (e.g., chatbots that care for senior citizens living alone, or counseling chatbots). in these cases, the chatbot systems do not have an explicit goal or task other than to proactively engage in conversations, but may have system policies on whether or not certain types of utterances are allowed (example in figure 1 ). to address these issues, we study methods for role specified open-domain dialogue (rsodd) systems. the goal of the system is conversing naturally with humans on open-ended topics while keeping conditions of given role. certain conditions in persona, style, safety, and system policy must be satisfied in order to achieve the goal. we consider a general and scalable framework to treat them, instead of using individual approaches to control each. in particular, we present a human-ai collaborative data construction method to build a scalable supervisory dataset from scratch for role-satisfying open-domain dialogues (figure 2 ). we propose to leverage large-scale lms for generating entire dialogue sessions between user and system by incontext few-shot learning manner @xcite , followed by humaninteractive correction processes. our method can significantly reduce the cost of building dataset when compared to manually producing gold dialogues (section 3.2). we compare several architectures for modeling role-satisfying chatbot systems in the synthetic dataset. in extensive experiments and ablation studies, we show that the proposed models considerably reduce undesirable utterances that violate the given role specification compared to the in-context learning baseline, while achieving competitive ssa @xcite ) scores for their responses. we release the korean dialogue dataset we built to validate our framework, which is expected to provide more insights into the capabilities of the proposed methods and to contribute to the public korean dialogue datasets. the contribution of our work is summarized as follows. pretrained lm in open-domain dialogue many prior works tried to pretrain the models on large-scale social comment chains data like reddit to model conversational behavior @xcite , 2020; @xcite , followed by finetuning on the diverse target dialogue dataset to improve engagingness and humanness @xcite . to avoid undesired behaviors of the models including toxicity and bias from the human-human conversation, they merely exclude some parts of training data using automatic filtering by predefined criteria. ## data construction in this section, we describe a framework to gather supervisory data for building rsodd systems. the input to the framework is a role specification described by the chatbot developer ( ## experiments we detail experimental settings and results in this section, including evaluations of the data collected by in-context few-shot learning (section 5.2), comparisons of model variants (section 5.3), and evaluations on system's response qualities (section 5.4). ## discussion although our methods achieve the low error rates in human interactive evaluations, the results have some limitations. the results should be regarded as the error rates of typical conversations without adversarial attack. because the annotators are in-structed to participate in the chat as if they were typical users, they did not try as many conversations that could induce toxic words from the model. this may be the reason why the toxicity is close to zero as shown in the human filtering process in the proposed data collection framework has room to be more efficient. since the accuracy of the classifier is comparable even when just 10% of the total data is used ( ## conclusion we present a framework for building role specified open-domain dialogue systems from scratch. we propose leveraging large-scale lms to generate supervisory datasets for training dialogue systems with arbitrary roles with minimal effort for manually composing dialogues. our research also analyzes several model architectures for the task. by extensive experiments, we demonstrate the effectiveness of the collected data and modeling approaches in terms of satisfying role constraints and improving dialogue abilities. we argue that our framework can be extended to implement dialogue systems with various roles and characters, even when available datasets are few.