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见方式，但类比推理本身是否是一项可习得的任务也值得探究。本文测试了多种学习基本类比推理的方法，重点聚焦于比常用自然语言处理基准更贴近人类类比推理评估的典型类比。实验发现，即使使用少量数据，模型也能学习类比推理。此外，我们将模型与含人类基线数据的数据集进行对比，发现训练后模型的性能趋近于人类水平。