  Figures of speech, such as metaphor and irony, are ubiquitous in literature
works and colloquial conversations. This poses great challenge for natural
language understanding since figures of speech usually deviate from their
ostensible meanings to express deeper semantic implications. Previous research
lays emphasis on the literary aspect of figures and seldom provide a
comprehensive exploration from a view of computational linguistics. In this
paper, we first propose the concept of figurative unit, which is the carrier of
a figure. Then we select 12 types of figures commonly used in Chinese, and
build a Chinese corpus for Contextualized Figure Recognition (ConFiguRe).
Different from previous token-level or sentence-level counterparts, ConFiguRe
aims at extracting a figurative unit from discourse-level context, and
classifying the figurative unit into the right figure type. On ConFiguRe, three
tasks, i.e., figure extraction, figure type classification and figure
recognition, are designed and the state-of-the-art techniques are utilized to
implement the benchmarks. We conduct thorough experiments and show that all
three tasks are challenging for existing models, thus requiring further
research. Our dataset and code are publicly available at
https://github.com/pku-tangent/ConFiguRe.
