Abstract
Unsupervised commonsense reasoning (UCR) is becoming increasingly popular as the construction of commonsense reasoning datasets is expensive, and they are inevitably limited in their scope. A popular approach to UCR is to fine-tune language models with external knowledge (e.g., knowledge graphs), but this usually requires a large number of training examples. In this paper, we propose to transform the downstream multiple choice question answering task into a simpler binary classification task by ranking all candidate answers according to their reasonableness. To this end, for training the model, we convert the knowledge graph triples into reasonable and unreasonable texts. Extensive experimental results show the effectiveness of our approach on various multiple choice question answering benchmarks. Furthermore, compared with existing UCR approaches using KGs, ours is less data hungry. Our code is available at this https URL.
Abstract (translated)
无监督常识推理(UCR)正在变得越来越流行，因为构建常识推理数据集的成本很高，不可避免地会受到限制。UCR的一个流行的方法是通过外部知识(例如知识图谱)优化语言模型，但这通常需要大量训练示例。在本文中，我们提议将后续多项选择回答任务转换为更简单的二进制分类任务，通过按合理性排序所有备选答案来这样做。为了训练模型，我们将知识图谱三元组转换为合理和不合理的文本。广泛的实验结果表明，我们的方法在各种多项选择回答基准测试中的有效性。此外，与使用KGs的现有UCR方法相比，我们的方法的数据需求较少。我们的代码可用在此处https://github.com/lihaoyi21/UCR代码库中。
URL
https://arxiv.org/abs/2305.15932
