Metaphor Detection with Context Enhancement and Curriculum Learning (2024)

Abstract: Metaphor detection is a challenging task for natural language processing (NLP) systems. Previous works failed to sufficiently utilize the internal and external semantic relationships between target words and their context. Furthermore, they have faced challenges in tackling the problem of data sparseness due to the very limited available training data. To address these two challenges, we propose a novel model called MiceCL. By leveraging the difference between the literal meaning of the target word and the meaning of the sentence as the sentence external difference, MiceCL can better handle the semantic relationships. Additionally, we propose a curriculum learning framework for automatically assessing difficulty of the sentence with a pre-trained model. By starting from easy examples and gradually progressing to more difficult ones, we can ensure that the model will not deal with complex data when its ability is weak so that to avoid wasting limited data. Experimental results demonstrate that MiceCL achieves competitive performance across multiple datasets, with a significantly improved convergence speed compared to other models.

## introduction metaphor has long been a common phenomenon in language and cognition research @xcite . it not only exists in our daily communication, but also plays a key role in the effective understanding of abstract concepts. in our daily life, we often use metaphors to convey emotions, ideas and opinions to make communication more vivid, rich and profound. for example, when we say "time is money", we don't mean it literally as gold, but rather as a metaphor for how precious and finite it is. * *corresponding author however, metaphor detection is a complex and challenging task. it involves the integration of various contextual cues, considering factors such as vocabulary, grammar and context. this undoubtedly places high demands on the capabilities of models. metaphor detection is similar to word sense disambiguation, which involves determining whether a target word is used metaphorically or literally. however, unlike word sense disambiguation, which has abundant corpus resources, metaphor detection tasks suffer from very limited data availability, resulting in a severe data sparsity problem. in summary, the current metaphor detection task faces two major challenges: (1) how to effectively use sentence information such as context, grammar and context; (2) how to solve the problem of data sparsity. these two challenges are not independent of each other. failing to effectively use sentence information will make the data sparsity problem more severe, and the data sparsity problem will also make it difficult for the model to learn truly effective information. recent methods @xcite partially address the first challenge by encoding sentence information through pre-trained transformer models and leveraging linguistic rules to enhance their representation of complex contextual meaning and various semantic information. however, they focus too much on designing complex structures to better encode sentence information, but ignore how to make full use of the existing data set in the case of sparse data, so they do not address the second challenge. also, the model they designed does not make good use of linguistic rules. clcl @xcite attempts to address the second challenge by introducing curriculum learning to make better use of the data by transitioning the model from simple to complex examples. however, clcl uses manual evaluation of difficulty to measure the difficulty of examples. not only is it too dependent on expert knowledge, but what is considered simple by humans may not be considered simple by machine models, that is, difficulty evaluation methods may not be suitable for machine models. therefore, the second challenge is not well addressed by clcl. to address the above two challenges simultaneously, we propose a novel metaphor detection model named metaphor identification with context enhancement and curriculum learning (micecl). it adopts new spv (selectional preference violation) @xcite and mip (metaphor identification procedure) @xcite to better solve the first challenge, which we call ie-spv (internal and external selectional preference violation) and m-mip (multiple metaphor identification procedure). ie-spv can make full use of the internal and external semantic relationships of the sentence, so as to better determine whether the target word is reasonable in the context. m-mip learns the basic and complex differences between the contextual meaning and the literal meaning of the target word through two parts respectively. by combining ie-spv and m-mip, we can make full use of complex contextual meaning and various semantic information to solve the first challenge. to address the second challenge, we introduce a curriculum learning framework that automatically computes the difficulty. by using the training loss of the pre-trained model as the difficulty, we eliminate the disadvantage of manually evaluating the difficulty and are able to more accurately reflect the difficulty of the sentence as considered by the machine model. we largely addressed the second challenge by transitioning the model from simple to complex examples to make the best use of the limited data. our main contributions are: ## micecl in this section, we will introduce our proposed model, which is mainly composed of two parts: ## conclusion in this paper, we propose a novel metaphor detection model called micecl. the proposed model includes two modules, m-mip and ie-spv, to identify metaphors by effectively capturing the basic and complex differences between target words and context information. in addition, we introduce curriculum learning to automatically measure the difficulty with a pre-trained model to re-arrange the training order, eliminate the disadvantages of manual evaluation of difficulty, and largely solve the problem of data sparsity. we evaluate our model on four datasets, and the effect shows a significant improvement over the strong baselines. we provide detailed ablation experiments to demonstrate the effectiveness of our approach.