Conversation Chronicles: Towards Diverse Temporal and Relational Dynamics in Multi-Session Conversations (2023)

Abstract: In the field of natural language processing, open-domain chatbots have emerged as an important research topic. However, a major limitation of existing open-domain chatbot research is its singular focus on short single-session dialogue, neglecting the potential need for understanding contextual information in multiple consecutive sessions that precede an ongoing dialogue. Among the elements that compose the context in multi-session conversation settings, the time intervals between sessions and the relationships between speakers would be particularly important. Despite their importance, current research efforts have not sufficiently addressed these dialogical components. In this paper, we introduce a new 1M multi-session dialogue dataset, called Conversation Chronicles, for implementing a long-term conversation setup in which time intervals and fine-grained speaker relationships are incorporated. Following recent works, we exploit a large language model to produce the data. The extensive human evaluation shows that dialogue episodes in Conversation Chronicles reflect those properties while maintaining coherent and consistent interactions across all the sessions. We also propose a dialogue model, called ReBot, which consists of chronological summarization and dialogue generation modules using only around 630M parameters. When trained on Conversation Chronicles, ReBot demonstrates long-term context understanding with a high human engagement score.

## introduction open-domain conversation is one of the important research topics. by deploying conversation systems in our daily lives, we can enjoy automated services like counseling, language tutoring, etc. there has been much research effort to build such ai 1 our data/code are publicly available at https:// conversation-chronicles.github.io/ a couple of years after ⏰ session n-1 sounds good to me. i need to cool down after working in this heat all day. thanks. cheers! hey, let's take a break and have a beer. here you go, one cold beer for my hard-working colleague. session n i know it's tough, ⋯⋯ and i'm sure your boss will understand. anytime. remember when we had that relaxing moment with a couple of beers after working in the sun all day? maybe we can have a similar moment once you're out of the hospital. conversation @xcite @xcite . however, although these chatbot models produce human-like fluent responses, they seem to have a limited ability that only understands short-term dialogue context, making them less applicable in realworld scenarios in which long-term conversational situations are often encountered. specifically, they do not care about the context of past conversations and only generate responses based on an ongoing dialogue (so-called single-session dialogue). to address these issues, the multi-session con-versation has been proposed @xcite . multi-session conversation comprises consecutive sessions that make a coherent dialogue episode. in a multi-session conversation, each session is assumed to occur serially with a time interval in between. time interval plays an important role to infuse dynamics in a conversational interaction between speakers. for instance, depending on the time elapsed since the last conversation, their responses about past events would vary. however, previously introduced works have a relatively short range of time intervals, limiting types of transitions from the past sessions. also, to our best knowledge, there is no research effort to incorporate the relationship between speakers into conversations. the relationship can significantly rule the way they perceive and interact with each other, giving an additional dimension of dynamics to a dialogue. therefore, we introduce conversation chronicles, a new high-quality long-term conversation dataset that consists of 1m multi-session dialogues (200k episodes; each episode has 5 dialogue sessions). conversation chronicles features more diverse chronological context and fine-grained speaker relationships. time interval in conversation chronicles includes varying ranges from a few hours to even years, allowing to cover a longer elapsed time than previous multisession dialogue settings. also, various relationships induce varied events and interaction flows to the conversations, which facilitates the application to different real-world scenarios (figure 1 ). on the other hand, collecting data samples, which requires sophisticated interaction, at scale is not easy and time-consuming. thus, recent works are getting turning to exploit large language models (llms) to collect such complicated data in an automated way by designing refined query methods @xcite @xcite @xcite . following those works, we collect our multi-session conversation dataset through well-defined prompts to llms. 2 to be specific, each prompt consists of relationships, event descriptions, and time intervals so that the created dialogues incorporate those components. according to human evaluation based on multiple criteria, our conversation chronicles is preferred to other multi-session conversation datasets. we also propose a new multi-session conversation model, rebot. this model uses only about 630m parameters and reflects the chronological and relational dynamics in the long-term conversation setting. the extensive human evaluation shows that its responses are preferred over other chatbot models in long-term conversational situations. our contributions in this study are: long-term conversation. current open-domain dialogue models learn from short conversations with little context, which has the obvious limitation that they will not remember the information for future conversations. to address these issues, there are attempts to add modules to the standard architecture or propose datasets for long-term situations. @xcite proposes a method of extracting and managing user information from dialogues. @xcite proposes a chinese multi-turn dataset dulemon and a persona memory-based framework plato-ltm. @xcite proposes the first multi-session dataset, called msc, with time intervals between sessions. also, bae ## conversation chronicles in this study, we introduce a new high-quality longterm multi-session conversation dataset, called conversation chronicles. our dataset consists of 1m multi-session dialogues, comprising a total of 200k episodes, each of which consists of 5 sessions. we construct our dataset using the following process. ## rebot we propose a novel multi-session dialogue model, rebot (remember chatbot). rebot consists of two parts: the chronological summarization module and the dialogue generation module. the sum- metrics avg std consistency 4.41 0.80 coherence 4.04 1.06 time interval 4.46 0.77 relationship 4.40 1.08 overall 4.33 marization module provides the context of the previous sessions by concisely describing the chronological events. the dialogue generation module produces the next response reflecting chronological dynamics presented by the summarization module. ## results in this section, we present the evaluations of our rebot's performance in different experiment setups (please see section 3.4 for the quality of our dataset, conversation chronicles). ## conclusion we introduce a large-scale multi-session conversation dataset, conversation chronicles, which implements chronological dynamics by integrating time interval and speaker relationship in it. to create the multi-session conversations, we first build an event graph and then distill a series of dialogues from chatgpt using well-defined prompts based on the event graph, time interval, and speaker relationship. we verify the quality of our dataset with extensive human evaluations on diverse metrics and criteria. we also propose rebot, a multi-session dialogue model, which comprises chronological summarization and dialogue generation modules. the results of human evaluations show our rebot can generate diverse coherent responses according to different time intervals and speaker relationships with high user engagement without contradiction in a long-term conversation setup.