KEEP CHATTING ! An Attractive Dataset for Continuous Conversation Agents (2024)

Abstract: Ongoing chatting is an important step for conversational agents to build long-term connections with people. However, people tend to quickly lose interest in chatting if the conversational agent’s words are not engaging enough. In this paper, we present a novel task of increasing users’ willingness to continue talking to the agent.We collect a dataset named ContinuousChat by: (i) collecting personas and revising them, and then expanding the personas to detailed-personas through experiences, daily life, future plans, or interesting stories; (ii) expanding detailed-personas into the dialogues, and inject emotions and feelings into them; (iii) rewriting the dialogues in specific styles through few-shot prompt, conditioning on handwritten style-specific examples.We benchmark LLMs on ContinuousChat Dataset using both fine-tuning and in-context learning settings. Experiments over publicly available models demonstrate that although there is substantial room for improvement in generating style-specific dialogues, our ContinuousChat dataset is valuable in guiding conversational agents to generate more attractive dialogues and increase users’ willingness to continue the conversations.

## introduction open-domain dialogue is a longstanding challenge in natural language processing (nlp) that has garnered widespread interest from researchers. numerous approaches have been explored, and recently, generation models trained on large-scale datasets have gained significant attention @xcite @xcite @xcite . maintaining ongoing conversations is crucial for conversational agents to establish longterm connections with users. however, people tend to quickly lose interest if the conversational agent's responses are not sufficiently engaging. one common solution is to endow chatbot with a configurable persona. existing works about persona dialogue such as personachat @xcite have greatly facilitated the chatbot with configurable and persistent personalities. dulemon @xcite focuses not only on the consistency of the bot's own persona but also on the active construction and utilization of the user's persona in a long-term interaction. we argue that most current open-domain dialogue datasets primarily feature "two strangers trying to get to know each other", and there is a lack of conversations designed to teach agents how to generate more engaging dialogues that increase users' willingness to continue interacting with the agent. additionally, the language styles in these datasets are quite similar despite featuring various personas. moreover, most personas lack detailed characteristics, making it challenging to create a lasting impression on users. motivated by the aforementioned limitations, we would like to investigate the following research question: can we have better ways of increasing users' willingness to continue talking to the agent? figure 1 shows an example to illustrate the motivation of this paper. in this paper, we introduce a dataset named con-tinuouschat, which consists of engaging conversations. we begin by collecting and refining personas, then expand these base personas into detailed personas through descriptions of experiences, daily life, future plans, and interesting stories. subsequently, we develop these detailed personas into dialogues, infusing them with emotions and feelings. finally, we rewrite the dialogues in specific styles using few-shot prompts, conditioned on handwritten style-specific examples. to validate the usefulness of the created dataset, we benchmark large language models (llms) on the continuouschat dataset using both finetuning and in-context learning settings. experiments with publicly available models demonstrate that our continuouschat dataset effectively guides conversational agents in generating more engaging dialogues, thereby increasing users' willingness to continue conversations. the key contributions of this paper are summarized as follows. style-control generation. previous works have shown that focus on positive style result in engaging chats @xcite . @xcite proposed a set of models for generating polite dialogues. the models are guided by a politeness classifier in generating responses. su et al. (2021) first extracted a non-stylistic prototype from a generic dialogue system and then generate stylistic response via gpt-2. zheng et al. (2021) attempted to using monolingual stylistic data to increase the style intensity of dialogue response. dathathri et al. (2020) proposed a plug-and-play method (pplm) to control text style, which is an iterative generation method using a classifier on top of a pre-trained generation model. another work that is achieving fine-grained control using a very large architecture is the ctrl model (keskar et al., 2019). the style conditioning relies on control codes obtained from the training data (meta-data). lample et al. proposed a style transfer architecture using noisy encoders and decoders and style conditioning through an additional token. character a's personality is...(detailed persona), please let character a and character b start a dialogue around a's personality, no less than 10 sentences, each sentence no more than 30 words, pay attention to control the number of words. please use parentheses to mark the emotion of each sentence examples of cross-talk style are as follows:...(handwritten style examples) please rewrite the following dialogue in cross-talk style, please do not change the format of the dialogue: ...(base conversations) focus on 'i have a pug named tea', talk about your past experience in 6 sentences, each sentence should not exceed 20 words, pay attention to control the number of words. (a) (b) (c) figure 2: examples of prompts during (a) personas expansion (b) base continuous-chat generation (c) style continuous-chat generation categories examples hometown/residence i grew up in shanghai family/partner my dad and i have a bad relationship study/work i'm in college wish/plans i want to be a marine biologist opinions/ideas i think everyone is equal before the law feelings/emotions i'm very lonely hobbies i like reading health i am colorblind pets i have a pug named tea food i am vegetarian idols my favorite singer is taylor swift good at/not good at i'm not good at communication like/dislike i like snow habits i always put on my left sock first dress i put on green nail polish travel i always travel alone social i hang out with my friends on weekends others i participated in a math competition note that these personas are in chinese, we translated them into english to make them easy to read. ## experiments we build multiple strong baselines in our experiments. specifically, we implement different generative models in two ways: fine-tuning and incontext learning. we use chinese-alpaca-pro-7b and chatglm2-6b for our dataset.