论文标题
Dimongen：多元化的生成常识性推理来解释概念关系
DimonGen: Diversified Generative Commonsense Reasoning for Explaining Concept Relationships
论文作者
论文摘要
在本文中，我们提出了Dimongen，旨在产生各种句子，描述各种日常情况下的概念关系。为了支持这一点，我们首先通过调整现有的CONSONGEN数据集来为此任务创建基准数据集。然后，我们提出了一个称为Moree的两个阶段模型来生成目标句子。 Moree由猎犬模型的混合物组成，该模型可以检索与给定概念相关的各种上下文句子，以及基于检索到的上下文产生不同句子的发电机模型的混合物。我们对Dimongen任务进行实验，并表明，在生成的句子的质量和多样性方面，更多的人表现优于强大的基准。我们的结果表明，Moree能够产生不同的句子，这些句子反映了概念之间的不同关系，从而使人们对概念关系有全面的理解。
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.
