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。