Personalized Topic Selection Model for Topic-Grounded Dialogue (2024)

Abstract: Recently, the topic-grounded dialogue (TGD) system has become increasingly popular as its powerful capability to actively guide users to accomplish specific tasks through topic-guided conversations. Most existing works utilize side information (e.g. topics or personas) in isolation to enhance the topic selection ability. However, due to disregarding the noise within these auxiliary information sources and their mutual influence, current models tend to predict user-uninteresting and contextually irrelevant topics. To build user-engaging and coherent dialogue agent, we propose a personalized topic selection model for topic-grounded dialogue, named PETD, which takes account of the interaction of side information to selectively aggregate such information for more accurately predicting subsequent topics. Specifically, we evaluate the correlation between global topics and personas and selectively incorporate the global topics aligned with user personas. Furthermore, we propose a contrastive learning based persona selector to filter relevant personas under the constraint of lacking pertinent persona annotations. Throughout the selection and generation, diverse relevant side information is considered. Extensive experiments demonstrate that our proposed method can generate engaging and diverse responses, outperforming state-of-the-art baselines across various evaluation metrics.

## introduction the dialogue systems have been widely used for task-specific interactions like customer service @xcite or emotional interaction @xcite . early works adopt a passive stance in re-sponse to user queries, yielding generic or irrelevant responses @xcite . however, real-world conversational scenarios often manifest heightened complexity, necessitating dialogue agents to adeptly manage topics and actively guide conversations. although large language models, such as chatgpt, exhibit closely resemble abilities of humans, they fall short in topic management and exhibit suboptimal initiative @xcite . consequently, topic-grounded dialogue systems (tgds), which can proactively predict appropriate future topics and generate diverse and informative responses around new topics, have recently attracted considerable attention. indeed, the core of topic-grounded dialogue systems lies in the effective exploitation of diverse side information (i.e., global topics or user personas) to precisely predict subsequent topics. the former @xcite @xcite fully models the topic transitions overall topic sequences and infers the subsequent topic via the co-occurrences of adjacent topics over such topic transferring information, and the latter @xcite takes account of all user personas to model user preferences for topic selection. despite achieving promising results, previous studies still face challenges in adequately modeling side information. this limitation arises from inadequate consideration of the interconnections between different side information, leading to the indiscriminate integration of both relevant and irrelevant information for topic selection. this issue can be delineated from two perspectives. firstly, users with different personas may select different topics based on the same history topics. as shown in figure 1 (a), global co-occurrence topic frequency dis-r: i completely understand your yearning for love. first love is an unforgettable experience filled with innocence and passion. figure 1: the irrelevant information in global topics and user personas misleads the model to choose depersonalized and unsmooth topics. the topics are bolded and italicized. to address these problems, we propose a novel method called personalized topic selection model for topic-grounded dialogue (petd), which effectively mitigates the impact of irrelevant side information by considering the interaction among topics and personas for more accurately predicting subsequent topics. to disentangle the global topics that emerge from overlapping numerous personas, we establish the n-to-n correspondence between topics and personas at global level. we further selectively aggregate global topics aligned with user personas for efficiently filtering out irrelevant global topics to user personas and more accurate user preference modeling. to prevent the intrusion of context-irrelevant personas during topic selection, we employ a persona selector to predict personas likely to be manifested by the user in the next turn based on the topic path and dialogue context. in light of existing research, which indicates challenges in ensuring optimal training for each submodule through end-to-end single-task supervised training alone @xcite , we design a contrastive learning based auxiliary task to address the impediment of lacking relevant persona annotations for targeted training of the persona selector submodule. this task aims to specifically optimize the persona selector and amplify the representation distinction between different personas. finally, we utilize side information after filtering out irrelevant information to assist topic selection and dialogue generation. to summarize, the contributions of this paper are listed as follows: ## related work nowadays, existing methods in tgd often share a paradigm that decomposes the task into two related sub-task, namely topic selection and response generation @xcite @xcite . in this work, we mainly focus on the topic selection task, which can be broadly categorized into knowledge-based, global topic-based and user-based methods. knowledge-based methods. @xcite first propose the target-guided dialogue topic selection task and develop a rule-based model based on the similarity of the next topic and target. dkrn @xcite further utilizes semantic correlation to improve the smoothness of selected topics. @xcite and ckc @xcite model topic transition as a continuous walk on the commonsense graph (ckg), which effectively reduces the neighborhood candidate topic space. considering topic selection based on neighborhood entities on ckg does not conform to real dialogues, eccf @xcite ) mines highfrequency topic transition from real dialogues to expand commonsense graph. however, strict neighborhood constraints in topic selection limit the generalization of these methods to real-world conversations @xcite . ## conclusion in this work, we leverage the interplay between topics and personas to improve the accuracy of topic selection by removing redundant noise in side information. we notice that user under different personas selects different topics, and existing global topic-based methods ignore this difference. simultaneously, the complicated persona information in the user profile contains plenty of noise, and only a few are related to the next topic. to tackle the above problems, we propose a novel model, named petd. we construct a corresponding topic set for each persona at the global level and selectively amalgamate globally pertinent topic sets aligned with user personas to exclude persona-irrelevant global topics. we subsequently develop a persona selector, curbing the adverse influence of irrelevant personas on topic selection. a contrastive learning based auxiliary task is proposed to optimize the persona selector and increase the distance between different persona representations in unlabeled scenarios. comprehensive experiments showcase the superior ability of our method to achieve more precise topic selection and produce captivating and varied responses, outperforming all benchmarks across various evaluation metrics.