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
this https URL.

该研究提出了 “隐喻单元” 的概念，旨在从话语层面上提取隐喻单元，并将其分类为正确的隐喻类型。在 “ConFiguRe” 上，进行了三项任务，即隐喻单元提取、隐喻类型分类和隐喻识别，并展示了现有模型在这三项任务上的不足，需要进一步研究。

ConFiguRe: 探索汉语语篇层级的修辞手法

ConFiguRe: Exploring Discourse-level Chinese Figures of Speech

Figurative language generation is the task of reformulating a given text in
the desired figure of speech while still being faithful to the original
context. We take the first step towards multi-figurative language modelling by
providing a benchmark for the automatic generation of five common figurative
forms in English. We train mFLAG employing a scheme for multi-figurative
language pre-training on top of BART, and a mechanism for injecting the target
figurative information into the encoder; this enables the generation of text
with the target figurative form from another figurative form without parallel
figurative-figurative sentence pairs. Our approach outperforms all strong
baselines. We also offer some qualitative analysis and reflections on the
relationship between the different figures of speech.

提出了一种基于 BART 的 multi-figurative 语言预训练框架 mFLAG，同时提供了五种常见英文比喻形式的自动生成基准，实验结果表明该方法优于强基线，并对不同语言形式之间的关系进行了一些定性分析和思考。