  Figurative and metaphorical language are commonplace in discourse, and
figurative expressions play an important role in communication and cognition.
However, figurative language has been a relatively under-studied area in NLP,
and it remains an open question to what extent modern language models can
interpret nonliteral phrases. To address this question, we introduce Fig-QA, a
Winograd-style nonliteral language understanding task consisting of correctly
interpreting paired figurative phrases with divergent meanings. We evaluate the
performance of several state-of-the-art language models on this task, and find
that although language models achieve performance significantly over chance,
they still fall short of human performance, particularly in zero- or few-shot
settings. This suggests that further work is needed to improve the nonliteral
reasoning capabilities of language models.
