Simile interpretation (SI) and simile generation (SG) are challenging tasks
for NLP because models require adequate world knowledge to produce predictions.
Previous works have employed many hand-crafted resources to bring
knowledge-related into models, which is time-consuming and labor-intensive. In
recent years, pre-trained language models (PLMs) based approaches have become
the de-facto standard in NLP since they learn generic knowledge from a large
corpus. The knowledge embedded in PLMs may be useful for SI and SG tasks.
Nevertheless, there are few works to explore it. In this paper, we probe simile
knowledge from PLMs to solve the SI and SG tasks in the unified framework of
simile triple completion for the first time. The backbone of our framework is
to construct masked sentences with manual patterns and then predict the
candidate words in the masked position. In this framework, we adopt a secondary
training process (Adjective-Noun mask Training) with the masked language model
(MLM) loss to enhance the prediction diversity of candidate words in the masked
position. Moreover, pattern ensemble (PE) and pattern search (PS) are applied
to improve the quality of predicted words. Finally, automatic and human
evaluations demonstrate the effectiveness of our framework in both SI and SG
tasks.

本文介绍一种利用预训练语言模型中的知识解决类比解释和类比生成任务的方法，提出一种基于模式的方法来完成词的预测，同时采用模式集成和模式搜索的方式提高预测词的质量。实验结果表明该方法在类比解释和类比生成方面取得了很好的效果。