  This paper examines the encoding of analogy in large-scale pretrained
language models, such as BERT and GPT-2. Existing analogy datasets typically
focus on a limited set of analogical relations, with a high similarity of the
two domains between which the analogy holds. As a more realistic setup, we
introduce the Scientific and Creative Analogy dataset (SCAN), a novel analogy
dataset containing systematic mappings of multiple attributes and relational
structures across dissimilar domains. Using this dataset, we test the
analogical reasoning capabilities of several widely-used pretrained language
models (LMs). We find that state-of-the-art LMs achieve low performance on
these complex analogy tasks, highlighting the challenges still posed by analogy
understanding.

Computation and Language;Machine Learning