Long Time No See! Open-Domain Conversation with Long-Term Persona Memory (2022)

Abstract: Most of the open-domain dialogue models tend to perform poorly in the setting of long-term human-bot conversations. The possible reason is that they lack the capability of understanding and memorizing long-term dialogue history information. To address this issue, we present a novel task of Long-term Memory Conversation (LeMon) and then build a new dialogue dataset DuLeMon and a dialogue generation framework with Long-Term Memory (LTM) mechanism (called PLATO-LTM). This LTM mechanism enables our system to accurately extract and continuously update long-term persona memory without requiring multiple-session dialogue datasets for model training. To our knowledge, this is the first attempt to conduct real-time dynamic management of persona information of both parties, including the user and the bot. Results on DuLeMon indicate that PLATO-LTM can significantly outperform baselines in terms of long-term dialogue consistency, leading to better dialogue engagingness.

## introduction persona is crucial for open-domain dialogue systems to establish long-term intimacy with users @xcite . existing persona dialogue datasets such as personachat @xcite and models @xcite @xcite have greatly facilitated the chatbot with configurable and persistent personalities. nevertheless, current open-domain dialogue systems still cannot build a long-term connection with humans. the possible reason is that they lack the capability of understanding and memorizing longterm dialogue history information, which we called • 叫我小张就行 (just call me xiaozhang) figure 1 : a sample of long-term conversation with memory. at first, the chat partner is not familiar with each other, so the goal is to get to know each other; then, after multiple sessions, the chatbot already has a certain understanding and memory of the user's persona and its own persona, making the deep chat possible. despite the importance and challenge of utilizing long-term persona in open-domain dialogue, as far as we know, the long-term persona ability of large-scale models is less studied due to a lack of both task design and corresponding dataset. previous long-term persona dialogue systems @xcite are mainly rule-based systems without large-scale pre-training models, in which researchers proposed various episodic memory architectures to extract, store and manage rel-evant facts in prior interactions for use in future dialogs @xcite . in addition, existing persona conversation datasets @xcite @xcite focus only on the consistency of the chatbot's own persona and ignore the memory and utilization of the user's persona. and they all set fixed persona that cannot be updated during the chat. recently, @xcite proposed msc dataset as a multi-session extension of personachat, and its sessions are additionally annotated with summaries of important personal points. similar to the previous episodic memory architecture, @xcite summarize and recall previous conversations for future dialogue generation. the stored documents in msc will not be dynamically modified and will increase infinitely as the conversation progresses. furthermore, the retrieval-augmented generative models rely on a long-session conversation dataset for training, which is expensive and difficult to annotate. to address the limitations of existing models and the above issues, we defines the lemon (longterm memory conversation) task and propose a new dataset named dulemon, which 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 (ie. mutual persona). we demonstrate an example dialogue in dulemon in figure 1 . in dulemon, we assume that the two speakers have previously interacted with each other and that the chatbot remembers part of the user's persona. besides, both the user and chatbot grounding persona are annotated in each utterance. based on our collected dataset, we carefully design a novel plato-ltm framework for the longterm persona dialogue setting by adding a plug-andplay long-term memory (ltm) to the state-of-theart open-domain dialogue model @xcite . it enables us to study long-term persona conversations without relying on the long-session dataset. plato-ltm can extract both parties' persona information from the conversation in real time, write it to persona memory respectively, and retrieve both parties' persona information from memory to generate responses. the plato-ltm framework consists of three modules: (1) persona extractor (pe): the memory is updated by filtering irrelevant information and extracting persona sentences through a classifier. (2) long-term memory (ltm): two separated long-term memories store the explicit persona information of interlocutors. (3) generation module: we use the large-scale model and the retrieved persona sentences of the user and chatbot are directly concatenated with dialogue context as model input. our major contributions are as follows: to support our task, we collect and release a new dataset, entitled dulemon. in dulemon, the chatbot actively remembers and reasonably uses what the user has said about their persona while maintaining consistency in its persona, allowing the conversation to proceed more deeply. in a nutshell, our dulemon dataset has two essential features: during the conversation, the chatbot can see the persona of both parties; the other is that the persona associated with the response is explicitly annotated in our dataset. unlike the personachat dataset, the setting in dulemon is that one speaker plays the role of a chatbot, and the other plays the user's role. we elaborate on the construction process of the dataset as the following. to scale the amount of data, we also collected conversations where the user's persona was not visible to the bot, following the personachat @xcite . finally, our dulemon dataset consists of two parts. in delemon-self, the bot only knows its own persona, while in dulemon-both, it also knows part of the user's persona (as described above). the overall statistics of the dulemon are shown in ## model architecture in this work, we propose a long-term memory dialogue system based on an explicit memory readwrite mechanism. it includes three parts: persona extractor, long-term persona memory, and generation module. through the read and write operations of the long-term memory module, the user's and chatbot's persona can be stored, updated, and read. the overall framework is shown in figure3. ## experiments in this section, we present the baselines, experiment settings, model comparisons, and results of experiments. ## conclusion in this paper, we present a novel lemon (longterm memory conversation) task and then build the corresponding dataset dulemon, introducing longterm persona modelling into large-scale generative dialogue models. we further propose a long-term memory (ltm) as a plug-in component of stateof-the-art large-scale generative dialogue models. ltm consists of user memory and chatbot memory, where the user memory is for understanding and memorizing persona information mentioned by the user, and the chatbot memory attempts to keep its persona information to be continuously updated over time. experiment results show that our system plato-ltm can make effective use of both parties' persona information from dialogue history to enhance dialogue consistency and engagingness when conducting a long-term conversation. in the future, we will further study the possibility of using reinforcement learning with human feedback signals to help long-term conversation.