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Analogies play a central role in human commonsense reasoning. The ability torecognize analogies such as "eye is to seeing what ear is to hearing",sometimes referred to as analogical proportions, shape how we structureknowledge and understand language. Surprisingly, however, the task ofidentifying such analogies has not yet received much attention in the languagemodel era. In this paper, we analyze the capabilities of transformer-basedlanguage models on this unsupervised task, using benchmarks obtained fromeducational settings, as well as more commonly used datasets. We find thatoff-the-shelf language models can identify analogies to a certain extent, butstruggle with abstract and complex relations, and results are highly sensitiveto model architecture and hyperparameters. Overall the best results wereobtained with GPT-2 and RoBERTa, while configurations using BERT were not ableto outperform word embedding models. Our results raise important questions forfuture work about how, and to what extent, pre-trained language models captureknowledge about abstract semantic relations.

BERT is to NLP what AlexNet is to CV: Can Pre-Trained Language Models Identify Analogies?