D imon G en: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships (2023)

Abstract: In this paper, we propose DimonGen, which aims to generate diverse sentences describing concept relationships in various everyday scenarios. To support this, we first create a benchmark dataset for this task by adapting the existing CommonGen dataset. We then propose a two-stage model called MoREE to generate the target sentences. MoREE consists of a mixture of retrievers model that retrieves diverse context sentences related to the given concepts, and a mixture of generators model that generates diverse sentences based on the retrieved contexts. We conduct experiments on the DimonGen task and show that MoREE outperforms strong baselines in terms of both the quality and diversity of the generated sentences. Our results demonstrate that MoREE is able to generate diverse sentences that reflect different relationships between concepts, leading to a comprehensive understanding of concept relationships.

## introduction concepts are mental representations of classes or categories of objects, events, or ideas, distinguished by shared characteristics that set them apart from other things. for instance, the concept of "dog" represents a class of animals that share characteristics such as being four-legged, having fur, and being domesticated. these concepts are crucial in helping us understand and communicate about the world around us. to fully grasp concepts, it is important to understand the relationships between them. researchers have proposed using generated sentences as a means to model these relationships more effectively @xcite @xcite . for example, common-gen @xcite aims to generate coherent 1 code and data are available at https://github. com/liuchenzhengyi/dimongen . * equal contribution. † corresponding author. ## dimongen: diversified generative commonsense reasoning we propose a task called dimongen that aims to generate diverse sentences that describe the relationships between a pair of concepts from different perspectives. the task is defined as a diverse constrained text generation task, where the input is a pair of concepts (i.e., @xmath0, e b }) and the output is a set of sentences @xmath1, . . . , y n } that include both concepts and are diverse in terms of their content (an example is illustrated in fig. 1 ). to solve the above task, we propose a two-stage retrieval-enhanced method named moree, which consists of a mixture of retriever and generator models (fig. 2 ). this method is based on the mixture of experts (moe) model, which will be reviewed in the following section before introducing moree. ## conclusion while previous approaches have used generated sentences to model concept/concept relationships, these methods often rely on a single sentence and can be insufficient or biased in conveying the complexity of these relationships. to address this issue, we propose dimongen, a task for generating diverse sentences that describe concept relationships in various everyday scenarios. to solve the proposed task, we design a two-stage model called moree, which combines a mixture of retriever and generator models. our experimental results demonstrate the effectiveness of moree in generating coherent and diverse sentences to describe concept relationships in everyday scenarios.