While analogies are a common way to evaluate word embeddings in NLP, it is also of interest to investigate whether or not analogical reasoning is a task in itself that can be learned. In this paper, we test several ways to learn basic analogical reasoning, specifically focusing on analogies that are more typical of what is used to evaluate analogical reasoning in humans than those in commonly used NLP benchmarks. Our experiments find that models are able to learn analogical reasoning, even with a small amount of data. We additionally compare our models to a dataset with a human baseline, and find that after training, models approach human performance.

翻译：虽然类比是自然语言处理中评估词嵌入的常用方法，但探究类比推理本身是否可作为一种可学习的任务同样具有重要意义。本文测试了多种学习基础类比推理的方法，特别聚焦于比常见NLP基准测试中更具人类类比推理评估典型性的类比类型。实验发现，模型仅需少量数据即可学会类比推理。我们进一步将模型与包含人类基准的数据集进行对比，结果显示经过训练后，模型的表现接近人类水平。