Metaphor generation is a challenging task which can impact many downstream
tasks such as improving user satisfaction with dialogue systems and story
generation. This paper tackles the problem of Chinese nominal metaphor
generation by introducing a multitask metaphor generation framework with
self-training and metaphor identification mechanisms. Self-training addresses
the data scarcity issue of metaphor datasets. That is, instead of solely
relying on labelled metaphor datasets which are usually small in size,
self-training helps identify potential metaphors from a large-scale unlabelled
corpus for metaphor generation. The metaphor weighting mechanism enables our
model to focus on the metaphor-related parts of the input (e.g., the comparison
of the metaphor and comparator) during model learning and thus improves the
metaphoricity of the generated metaphors. Our model is trained on an annotated
corpus consisting of 6.3k sentences that contain diverse metaphorical
expressions. Experimental results show that our model is able to generate
metaphors with better readability and creativity compared to the baseline
models, even in the situation where training data is insufficient.

本文介绍了一个多任务隐喻生成框架，包括自我训练和隐喻鉴别机制，用于解决中文命名隐喻生成的数据稀缺性问题。实验结果表明，与基准模型相比，我们的模型可以生成具有更好可读性和创造性的隐喻，即使训练数据不充足。

多任务学习的名词隐喻生成

Nominal Metaphor Generation with Multitask Learning

Generating metaphors is a difficult task as it requires understanding nuanced
relationships between abstract concepts. In this paper, we aim to generate a
metaphoric sentence given a literal expression by replacing relevant verbs.
Guided by conceptual metaphor theory, we propose to control the generation
process by encoding conceptual mappings between cognitive domains to generate
meaningful metaphoric expressions. To achieve this, we develop two methods: 1)
using FrameNet-based embeddings to learn mappings between domains and applying
them at the lexical level (CM-Lex), and 2) deriving source/target pairs to
train a controlled seq-to-seq generation model (CM-BART). We assess our methods
through automatic and human evaluation for basic metaphoricity and conceptual
metaphor presence. We show that the unsupervised CM-Lex model is competitive
with recent deep learning metaphor generation systems, and CM-BART outperforms
all other models both in automatic and human evaluations.

本研究旨在基于概念隐喻理论通过控制生成过程并使用两种方法 (词汇级和序列生成模型) 生成有意义的隐喻表达。经过自动化和人工评估，实验结果表明，无监督的 CM-Lex 模型与最新的深度学习隐喻生成系统具有竞争力；而 CM-BART 优于其他所有模型。

概念映射的隐喻生成

Metaphor Generation with Conceptual Mappings

Generating metaphors is a challenging task as it requires a proper
understanding of abstract concepts, making connections between unrelated
concepts, and deviating from the literal meaning. In this paper, we aim to
generate a metaphoric sentence given a literal expression by replacing relevant
verbs. Based on a theoretically-grounded connection between metaphors and
symbols, we propose a method to automatically construct a parallel corpus by
transforming a large number of metaphorical sentences from the Gutenberg Poetry
corpus (Jacobs, 2018) to their literal counterpart using recent advances in
masked language modeling coupled with commonsense inference. For the generation
task, we incorporate a metaphor discriminator to guide the decoding of a
sequence to sequence model fine-tuned on our parallel data to generate
high-quality metaphors. Human evaluation on an independent test set of literal
statements shows that our best model generates metaphors better than three
well-crafted baselines 66% of the time on average. A task-based evaluation
shows that human-written poems enhanced with metaphors proposed by our model
are preferred 68% of the time compared to poems without metaphors.

通过使用并行语料库以及基于序列到序列模型和隐马尔可夫模型的方法，本文构建了一个自动跨领域语义映射的框架，通过生成一些具有语义相似性的例子，实现了提取图形用户界面元素的语义。