Similes play an imperative role in creative writing such as story and
dialogue generation. Proper evaluation metrics are like a beacon guiding the
research of simile generation (SG). However, it remains under-explored as to
what criteria should be considered, how to quantify each criterion into
metrics, and whether the metrics are effective for comprehensive, efficient,
and reliable SG evaluation. To address the issues, we establish HAUSER, a
holistic and automatic evaluation system for the SG task, which consists of
five criteria from three perspectives and automatic metrics for each criterion.
Through extensive experiments, we verify that our metrics are significantly
more correlated with human ratings from each perspective compared with prior
automatic metrics.

本研究建立了 HAUSER，一个全面自动的评估系统用于比喻生成任务，其中包括三个角度的五个标准和每个标准的自动衡量标准，通过广泛的实验，我们验证了我们的度量标准与每个角度的人类评分显著相关比先前的自动度量标准。

HAUSER: 面向综合和自动评价的比喻生成技术研究

HAUSER: Towards Holistic and Automatic Evaluation of Simile Generation

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。

从预训练语言模型中探索比喻知识

Probing Simile Knowledge from Pre-trained Language Models

Literary tropes, from poetry to stories, are at the crux of human imagination
and communication. Figurative language such as a simile go beyond plain
expressions to give readers new insights and inspirations. In this paper, we
tackle the problem of simile generation. Generating a simile requires proper
understanding for effective mapping of properties between two concepts. To this
end, we first propose a method to automatically construct a parallel corpus by
transforming a large number of similes collected from Reddit to their literal
counterpart using structured common sense knowledge. We then propose to
fine-tune a pretrained sequence to sequence model, BART~\cite{lewis2019bart},
on the literal-simile pairs to gain generalizability, so that we can generate
novel similes given a literal sentence. Experiments show that our approach
generates $88\%$ novel similes that do not share properties with the training
data. Human evaluation on an independent set of literal statements shows that
our model generates similes better than two literary experts
\textit{37\%}\footnote{We average 32.6\% and 41.3\% for 2 humans.} of the
times, and three baseline systems including a recent metaphor generation model
\textit{71\%}\footnote{We average 82\% ,63\% and 68\% for three baselines.} of
the times when compared pairwise.\footnote{The simile in the title is generated
by our best model. Input: Generating similes effortlessly, output: Generating
similes \textit{like a Pro}.} We also show how replacing literal sentences with
similes from our best model in machine generated stories improves evocativeness
and leads to better acceptance by human judges.

本文提出了一种自动构建平行语料库、用预训练的序列转序列模型 fine-tune 的方法，能够生成不同于训练数据中的新奇的明喻，而且相比两位文学专家和三种对比方法，能够更好地生成新奇的明喻，并展示了用生成的明喻替换机器生成的故事中的文字部分可以提高语言的唤起性和被人类接受程度。