CM -Gen: A Neural Framework for C hinese Metaphor Generation with Explicit Context Modelling (2022)

Abstract: Nominal metaphors are frequently used in human language and have been shown to be effective in persuading, expressing emotion, and stimulating interest. This paper tackles the problem of Chinese Nominal Metaphor (NM) generation. We introduce a novel multitask framework, which jointly optimizes three tasks: NM identification, NM component identification, and NM generation. The metaphor identification module is able to perform a self-training procedure, which discovers novel metaphors from a large-scale unlabeled corpus for NM generation. The NM component identification module emphasizes components during training and conditions the generation on these NM components for more coherent results. To train the NM identification and component identification modules, we construct an annotated corpus consisting of 6.3k sentences that contain diverse metaphorical patterns. Automatic metrics show that our method can produce diverse metaphors with good readability, where 92% of them are novel metaphorical comparisons. Human evaluation shows our model significantly outperforms baselines on consistency and creativity.

## introduction metaphors are commonly used in human language. usually, metaphors compare two different kinds of objects or concepts with the intent to make the expression more vivid, or to make unfamiliar things easier to understand @xcite . according to contrastive studies of english and chinese, metaphors are especially crucial in chinese as there are fewer abstract words in chinese, so that people tend to express abstract meaning via metaphors @xcite . in this paper, we focus on the generation task of a special type of chinese metaphor -nominal metaphors (nms). nms (比喻 in chinese) 1. 这个 @xcite tenor @xcite vehicle this @xcite tenor is as strong as a [bull] vehicle . nominal 2. @xcite tenor 好比[旅行] vehicle , 没有计划就难以前行 @xcite tenor is a [journey] vehicle , we cannot move on without a plan. ## method given an object or concept as a starting tenor, a chinese nominal metaphor will be generated consisting of four nm components: a comparison between tenor and vehicle linked with a com-parator and a context as an explanation for the comparison. the overall multitask framework is shown in figure 1 . we can roughly divide our framework into four elements: 1) the gpt2 (radford et al., 2019) pre-trained language model; and three task-specific fully-connected layers used for 2) nm identification; 3) nm components identification; and 4) nm generation. ## conclusion in this paper, we propose a novel multitask framework for chinese nominal metaphor generation. our model seeks to jointly optimize three tasks including nm identification, nm component identification, and nm generation, which is effective in generating diverse nms with good coherence and readability. extensive experiments show that our model can significantly outperform baselines in terms of both consistency and creativity. additionally, we publish an annotated corpus for chinese nominal metaphors. future directions can be trying the usage of syntactic features and controllable nm generation. moreover, we would also like to evaluate the effect of metaphor generation in downstream tasks, such as story generation, dialog systems @xcite , and educational applications @xcite . for autumn: "passion, gold" etc. we then 2) construct literal expressions using the tenor and its properties. for example, "autumn is a symbol of passion" is obtained.