Persona Expansion with Commonsense Knowledge for Diverse and Consistent Response Generation (2023)

Abstract: Generating diverse and consistent responses is the ultimate goal of a persona-based dialogue. Although many studies have been conducted, the generated responses tend to be generic and bland due to the personas’ limited descriptiveness. Therefore, it is necessary to expand the given personas for more attractive responses. However, indiscriminate expansion of personas threaten the consistency of responses and therefore reduce the interlocutor’s interest in conversation. To alleviate this issue, we propose a consistent persona expansion framework that improves not only the diversity but also the consistency of persona-based responses. To do so, we define consistency criteria to avoid possible contradictions among personas as follows: 1) Intra-Consistency and 2) Inter-Consistency. Then, we construct a silver profile dataset to deliver the ability to conform with the consistency criteria to the expansion model. Finally, we propose a persona expansion model with an encoder-decoder structure, which considers the relatedness and consistency among personas. Our experiments on the Persona-Chat dataset demonstrate the superiority of the proposed framework.

## introduction in the field of open-domain dialogues, maintaining long-term interaction with users by generating human-like responses has been a persistent goal. as efforts along this line of research, persona-based dialogue models aim at generating more engaging and consistent responses based on personal traits @xcite . as the most widely used benchmark dataset, persona-chat @xcite consists of personal profiles, which contain 3-5 persona sentences (hereinafter referred to as "personas"), and dialogues conditioned on personas. with the persona-chat, many researches have attempted to improve the quality of responses from various perspectives @xcite : an example of persona expansion with commonsense. the center circle is the predefined profile, and the dashed circle is the valid expandable range. 2020; @xcite @xcite . however, since the descriptiveness of personas is limited, they have difficulties in converting what personas imply into attractive responses. unlike dialogue models, humans converse beyond the explicit persona itself through rational reasoning with commonsense knowledge. the commonsense reasoning ability is essential for making an interlocutor engage in conversations with a dialogue agent. figure 1 illustrates an example of persona expansion through commonsense reasoning. given the persona 'i am an athlete', it is possible to infer the characteristic 'sporty and active' with high probability. the implications of 'want to be the best athlete' and 'need to train hard' are also predictable. in order to mimic human reasoning ability, previous works expand original personas @xcite or infer new personas from dialogue utterances @xcite . however, the consistency among expanded personas was not explicitly considered. to prolong a conversation, both the engagingness and the consistency of responses are very important. therefore, we define the two consistencies that must comply with while improving the diversity of responses as follows: 1) intra-consistency and 2) inter-consistency. first, the intra-consistency means that expanded personas must not contradict the original persona. if the response conditioned on the expanded persona contradicts the original persona, the interlocutor loses interest in the conversation. second, the inter-consistency means that expanded personas must not contradict other personas in the same profile. for instance, as shown in figure 1 , the attribute of 'healthy' can be expanded from the original persona of 'i am an athlete'. however, considering the other persona in the profile, 'i have a leg injury', this comes to an inappropriate expansion. therefore, it is necessary to follow the intra-and inter-consistency when expanding personas to improve the persona-based responses. to achieve the above goals, we propose a novel persona expansion framework with commonsense knowledge (peck). peck aims to not only improve the diversity of responses but also avoid the problem of contradictions among personas. both abilities to understand the relatedness and to reason with commonsense knowledge are required to satisfy the consistency criteria. unfortunately, there are no supervised training datasets for persona expansion. furthermore, it costs a lot to annotate them to train both abilities. therefore, we build a silver profile dataset (see section 4) similar to the gold profile in the persona-chat, with simple manipulation of the commonsense knowledge graph anion @xcite . in addition, we propose a persona expansion model (see section 5) consisting of an encoder and a decoder for consistent expansion. the bidirectional encoder learns to understand the relatedness among personas within the same profile. then, the auto-regressive decoder learns to expand personas consistently with negative log-likelihood (nll) and unlikelihood (ul) @xcite training objectives. finally, we conduct extensive experiments on persona expansion and persona-based response generation. experimental results confirm the superiority of the proposed consistent expansion framework. the contributions of this work are as follows: ## problem statement in this paper, our goal is to generate expanded personas, which contribute to improving the quality of persona-based responses. to do so, the consistency criteria that expanded personas must comply with are as follows: 1) intra-consistency and 2) inter-consistency. formally, given an original persona p @xmath0, where @xmath1, p 2 , ..., p n } is the predefined profile, a persona expansion model aims to generate expanded personas e @xmath2, e 2 , ..., e m }, which do not contradict the original persona p i and all the remaining personas in profile p . for the model to learn these consistencies, we need original-expandable persona pairs with a positive or negative label, and the profile where the original persona belongs. that is to say, the training dataset should contain {original personas (profile), expandable personas, label}. regrettably, there are no training datasets of this form, and human annotation is expensive. hence, we construct a new dataset, silver profile, appropriate for our expansion goal through simple manipulation on anion, the existing commonsense knowledge graph. the detailed construction process will be described in section 4. finally, our model trained with the silver profile aims to generate expanded personas consistently from gold profiles within persona-chat. ## data construction leveraging anion, we construct a new dataset for training consistent persona expansion. through exploratory analysis on persona-chat, we identified some prominent features of profiles. first, each profile is mostly made up of personas, which are correlated or at least non-contradictory to one another. in the gold profile example in ## conclusions and future work in this paper, we proposed a novel persona expansion framework to improve the diversity and consistency of the persona-based dialogue. leveraging the existing commonsense knowledge graphs, we built a silver profile dataset to deliver the ability to conform with the consistency criteria (intra-/inter-consistency) to the expansion model. the expansion model of the encoder-decoder structure was trained using the silver profile dataset with unlikelihood training. after training, the model generated expanded personas that satisfy the consistency criteria. the experimental results proved that the proposed persona expansion is effective at improving the diversity of responses while keeping their consistency with personas. in the future, we plan to extend peck to leverage more external knowledge, such as conceptnet @xcite .