Concept-based Persona Expansion for Improving Diversity of Persona-Grounded Dialogue (2023)

Abstract: A persona-grounded dialogue model aims to improve the quality of responses to promote user engagement. However, because the given personas are mostly short and limited to only a few informative words, it is challenging to utilize them to generate diverse responses. To tackle this problem, we propose a novel persona expansion framework, Concept-based Persona eXpansion (CPX). CPX takes the original persona as input and generates expanded personas that contain conceptually rich content. We constitute CPX with two task modules: 1) Concept Extractor and 2) Sentence Generator. To train these modules, we exploit the duality of two tasks with a commonsense dataset consisting of a concept set and the corresponding sentences which contain the given concepts. Extensive experiments on persona expansion and response generation show that our work sufficiently contributes to improving the quality of responses in diversity and richness.

## introduction a persona-grounded dialogue model aims to generate more human-like and engaging responses based on given traits called persona @xcite . as efforts of this research line, many recent works have explored various approaches to improving the quality of persona-based responses @xcite @xcite . in spite of these efforts, there remain some limitations in persona-grounded dialogue models. they are in need of generating more diverse responses. due to the predefined personas being mostly short and limited to only a few informative words, the responses based on these personas tend to be generic and monotonous. to tackle this issue, compac @xcite expands the predefined personas with a commonsense knowledge graph about events, atomic @xcite . with the expanded personas, the dialogue agent generates ## task definition our goal is to generate expanded personas to solve the lack of information of a given original persona in persona-chat denoted as d p c . formally, given the original persona p i , a set of k concepts c @xmath0, c 2 , ..., c k } is extracted from p i , where p is a set of all personas in d p c . then, an expanded persona p + i is generated, using c i . we perform this expansion for all personas in p , constructing augmented persona-chat d + p c that includes all expanded personas p + . finally, d + p c is the augmented dataset that can enhance the dialogue in a data-driven manner. ## cpx framework the cpx framework consists of two task modules: 1) concept extractor -extracting concepts from the original persona; 2) sentence generator -generate multiple sentences from the given concepts. inspired by @xcite , we utilize the duality of the tasks to train these modules. however, since we just exploit the advantage of the task duality, two modules are independently trained without sharing parameters based on probabilistic duality. ## experiments to validate the performance of cpx, we conducted two experiments: 1) persona expansion and 2) response generation. ## discussions biases in persona selection while constructing the dataset for model training, we identified the bias in selecting the expanded personas. as shown in figure 4 , among the four types of personas expanded by comet, the ratio of each type selected through the nli model is uneven. while the personas expanded by cpx had been selected more evenly, the distribution was still somewhat biased. this suggests that it is likely for persona expansion models to generate more biased responses through poor selection. it also means that the approach that leverages nli-based similarities between expanded personas and predefined personas, queries, or gold responses, in the persona selection can also introduce some bias. in order to generate a richer and unbiased persona-based response, it is necessary to study a method of considering the fairness of this expanded persona selection process. ## conclusions and future work in this paper, we proposed the concept-based persona expansion framework to improve the semantic diversity of the persona-grounded dialogue. we expanded personas by extracting and utilizing semantically related concepts through concept-based commonsense reasoning, making persona-grounded dialogues more engaging. during the experiments, we identified some less relevant expansion cases. therefore, we have a plan to develop a method of preventing potential overexpansion. we also found that in order to effectively reflect the diversely expanded personas in response generation, it was necessary to resolve the bias in the selection process. from this perspective, we will conduct experiments and studies for models that can generate more effective persona-based responses through a bias-free selection process while maintaining the diversity of expanded personas.