Beyond Goldfish Memory: Long-Term Open-Domain Conversation (2022)

Abstract: Despite recent improvements in open-domain dialogue models, state of the art models are trained and evaluated on short conversations with little context. In contrast, the long-term conversation setting has hardly been studied. In this work we collect and release a human-human dataset consisting of multiple chat sessions whereby the speaking partners learn about each other’s interests and discuss the things they have learnt from past sessions. We show how existing models trained on existing datasets perform poorly in this long-term conversation setting in both automatic and human evaluations, and we study long-context models that can perform much better. In particular, we find retrieval-augmented methods and methods with an ability to summarize and recall previous conversations outperform the standard encoder-decoder architectures currently considered state of the art.

## introduction improvements in the ability to train large neural language models, together with the availability of larger and higher quality dialogue datasets, are spurring the development of increasingly convincing open-domain dialogue models @xcite . unfortunately, a major aspect missing from the current state of the art is that human conversations can take place over long time frames, whereas the currently used systems suffer in this setting. commonly used training and evaluation resources -while large in terms of number of training examples -include only short conversations, typically between 2-15 turns, consisting of a single conversational session. perhaps for that reason, the current state-of-the-art models such as meena @xcite and blenderbot @xcite employ transformers with token truncation lengths of only the 128 most recent tokens, and are clearly incapable of incorporating long-term conversational context. consequently, it is unclear how well these models will perform on long or multi-session open-domain conversations. in contrast, a successfully deployed bot will engage in many conversations over a length of time, as capturing organic user interest will garner continual reengagement from returning users. long-term open-domain communication gives the opportunity for the conversation to develop and even improve with time as the model has more context and more understanding of that specific user's interests. however current models, due to context truncation, will never use this information. in this work we study methods for long-term open-domain conversation. as to the best of our knowledge no public domain task exists to study such methods, we collect and release foot_0 a new english dataset, entitled multi-session chat (msc) that consists of human-human crowdworker chats over 5 sessions, with each session consisting of up to 14 utterances, where the conversationalists reengage after a number of hours or days and continue chatting. previous sessions are annotated with summaries of important personal points that may be useful in further conversations. when reengaging, conversationalists often address existing knowledge about their partner to continue the conversation in a way that focuses and deepens the discussions on their known shared interests, or explores new ones given what they already know. we study the performance of two long-context conversational architectures on this task: (i) retrieval-augmented generative models @xcite ; and (ii) a proposed read-write memory-based model that summarizes and stores conversation on the fly. we show that both techniques outperform conventional encoder-decoder transformers, and that training models on our new task give long-term conversational abilities that existing state-of-the-art models lack, as shown in both automatic metrics and human evaluations. we provide extensive experiments and ablations that study the reasons behind these improvements. ## related work a relatively large and growing number of either natural or crowdsourced datasets have been collected and used in open-domain dialogue research. these datasets focus on the vast array of different skills required by a dialogue agent, but conversations lengths are typically short. recent state-of-the-art open-domain dialogue agents have utilized daily dialogue @xcite , personachat @xcite , wizard of wikipedia @xcite and pushshift.io reddit @xcite ; see @xcite for a review of other datasets. the number of conversational turns in these datasets is in the range of 2-15 turns, we provide statistics of some of these datasets in several existing datasets explore the use of personal knowledge used as context to dialogue, which can be seen as a short, simple memory provided to the bot. in @xcite such personas were extracted from reddit and used to train agents. in @xcite personas were first crowdsourced, and speakers were asked to play those roles. other works have considered encoding personas into vector-based weights @xcite . in this work, we explore summarizing the longterm conversations that occur in order to store useful information about them. summarization is a rich field where the vast majority of work focuses on summarizing documents @xcite @xcite , for example summarizing in order to predict other relevant information @xcite , while there is some work on dialogue as well @xcite @xcite . standard transformers have a fixed context length which due to the all-vs-all self-attention mechanism becomes inefficient when it is too large. consequently, many existing pre-trained models have short token truncation lengths, e.g. 128 tokens, as in blenderbot @xcite and meena @xcite , or 1024 tokens, as in bart @xcite . a number of approaches have been proposed to ameliorate this issue. long-context transformers consider ways to speed up the self-attention mechanism @xcite @xcite and retrieval-augmented methods consider ways to select the pertinent parts of the context to consider @xcite @xcite which can also be related to earlier neural qa methods @xcite . ## multi-session chat to conduct research on long-term conversations, we require data to both train on and to evaluate models. we consider the natural case where two speakers chat online in a series of sessions as is for example common on messaging platforms. each chat session consists of 6-7 turns for each speaker. then, after a certain amount of (simulated) time has transpired, typically hours or days, the speakers resume chatting, either continuing to talk about the previous subject, bringing up some other subject from their past shared history, or sparking up conversation on a new topic. we consider this multi-session long conversation setup, and name our dataset multi-session chat (msc). ## experiments using session dialogue context we compare different context types in ## conclusion we have shown that existing dialogue models, both in terms of training data and models trained, fail to conduct long-term conversations adequately. our work investigates recent model architectures to ameliorate this issue, and collects a new crowdsourced task, multi-session chat to both train and evaluate these models. we show, in terms of both automatic metrics and human evaluations, that these long-context dialogue modeling approaches outperform the previous systems. future work should investigate further improvements to architectures for the long-context dialogue setting.