It’s Better to Teach Fishing than Giving a Fish: An Auto-Augmented Structure-aware Generative Model for Metaphor Detection (2022)

Abstract: Metaphor Detection aims to identify the metaphorical meaning of words in the sentence. Most existing work is discriminant models, which use the contextual semantic information extracted by transformers for classifications directly. Due to insufficient training data and corresponding paraphrases, recent methods focus on how to get external resources and utilize them to introduce more knowledge. Currently, contextual modeling and external data are two key issues in the field. In this paper, we propose **A**n **A**uto-**A**ugmented **S**tructure-aware generative model (**AAAS**) for metaphor detection, which transforms the classification task into a keywords-extraction task. Specifically, we propose the task of structure information extraction to allow the model to use the ‘structural language’ to describe the whole sentence. Furthermore, without any other external resources, we design a simple but effective auto-augmented method to expand the limited datasets. Experimental results show that **AAAS** obtains competitive results compared with state-of-the-art methods.

## introduction metaphors, representing abstract meanings of words rather than their basic meanings, are ubiquitous in our daily life @xcite . for instance, in the sentence "the boxer's job is to bounce people who want to enter the club.", the verb bounce means "forcing somebody to leave", which is quite different from its basic meaning "moving up and down". as an abstract way of describing something by referring to something else, this language phenomenon draws extensive scholarly attention in linguistics. hence, how to identify the metaphors has become a heated topic in nlp, with an aim to improve our understanding of natural language. this challenging task requires sufficient data and ingenious designs based on linguistic knowledge. for years, linguisticians tended to use two metaphor identification procedures: metaphor identification procedure (mip) @xcite and selectional preference violation (spv) @xcite . mip identifies metaphorical words based on whether their contextual meanings are contrasted with their basic meanings. spv identifies metaphorical words if the target word is distinctive in the context. based on these two procedures, most existing methods tend to encode the whole sentence and extract the corresponding hidden state of the target word, which is then used as the contextual meaning for classifications @xcite . to get a better representation, various word embeddings (e.g., elmo embedding @xcite ) and attention mechanisms are introduced into the input and structure of the model @xcite . however, due to the limited capacity of traditional encoders (e.g., bi-lstm, dnn), these methods cannot model the sophisticated meanings of target words in different contexts. with the rapid development of transformers @xcite , many methods started to use various pre-trained language models to get better contextual representations. for example, deep-met @xcite uses transformers to encode the global context and the local text context, respectively. at the same time, incorporating more linguistic knowledge while designing the model also attracts scholarly attention. melbert @xcite , for instance, combines mip and spv with roberta @xcite , a typical example of integrating linguistic knowledge with pre-trained models. based on the concept that a metaphor is a conceptual mapping between the source domain and the target domain @xcite , mrbert @xcite extracts the subject and the object of the metaphor through the syntax parser to construct a contextual relation representation. moreover, owing to the similarity between metaphor detection, aspect-based sentiment analysis @xcite , and word sense disambiguation @xcite , some studies adopt multi-task learning to further improve the models' sensitivity to metaphorical words @xcite . nevertheless, these methods rely on large amounts of data for fine-tuning. however, labeled data for metaphor detection is scarce due to labor-intensive and time-consuming labeling. consequently, most of them have to employ transfer learning for better results. various external resources are mined to introduce extra knowledge to cope with the issue of insufficient data. cate @xcite downloads the corpora from wikipedia and then generates pseudo-labels for training. mdgi @xcite takes advantage of dictionary definitions to create the list of glosses, which facilitates the model's understanding of targets. in terms of the experimental results, these methods solve the issue of insufficient labeled data to a certain extent. however, external resources are still hard to access, and the whole training stage is time consuming. to sum up, previous models are over-reliant on external resources and tools, but data is hard to obtain, and the training process takes too much time. meanwhile, all of the existing methods are discriminant. nearly all of them concatenate or add up the representation of contextual words directly and then input the result into the classifier, which may lose some essential connections between them. even though some early methods are based on seq2seq models @xcite , they regard metaphor detection as a word-level classification (sequence labeling task) and ignore the linguistic structure. the problems mentioned above motivated us to propose an auto-augmented structure-aware generative model (aaas) for metaphor detection. just as figure 1 shows, almost all of the existing discriminant methods directly concatenate or sum up the contextual relation representation, which may lead to a loss of structural information. given that, we adopted the generative approach, which models the structural information more accurately. specifically, in the process of decoding, the decoder of the generative model took sequential relationships and interrelationships in contextual structure into consideration. in order to adapt the training process for application scenarios of the generative model, we designed a special keywords-extraction task for training. considering that we can identify a metaphor by its subjects and objects in most circumstances, the task requires that the model summarizes the original sentence with structural terms. in other words, the model needs to describe the critical semantics of the whole sentence with subject, target, object, and classification results. as a result, the model itself can extract the structural information from the sentence, which makes it independent of external tools such as the syntax parser. in addition, we designed a simple but effective auto-augmented method based on the masked language model. the method can expand the dataset without any external resource, which fundamentally solves the problem of insufficient labeled data. to achieve a better performance, we added some structural rules to the expansion stage. in a word, we enhanced the model's capabilities so that it can extract structural information and expand datasets independently. just as our title says, "it's better to teach a man to fish than give him a fish." in summary, the contributions of this paper are as follows: (1) through a detailed analysis of the existing methods, we point out the problems in metaphor detection. (2) we propose an autoaugmented structure-aware generative model. to the best of our knowledge, it is the first time to apply the generative approach to metaphor detection and free the model from external resources. (3) we conduct experiments on several typical datasets for metaphor detection. extensive analytical experiments show the effectiveness of both the generative model and the auto-augmented method in improving prediction performance, even compared with those relying on large-scale external resources. ## related work the work related to our method can be categorized into three types: adopting multi-task learning, min-ing the structural information, and introducing external resources. adopting multi-task learning metaphor detection is quite similar to aspect-based sentiment analysis and word sense disambiguation, because they all require the model to classify data according to the target word and sentence. through multitask learning, the knowledge learned from auxiliary tasks (e.g., absa, wsd, and so on) can promote the training stage of the major task (md) @xcite . nevertheless, there are still some differences between these tasks. for example, metaphor words are usually identified according to the context, including their subjects and objects, whereas sentiment polarities are determined solely based on adjectives with strong emotions. therefore, these similar tasks are not the most appropriate auxiliary tasks for metaphor detection. ## proposed method metaphor detection requires determining whether the target in the sentence is a metaphor word. given a sentence s consisting of n words @xmath0, w 2 , ..., w n } and the target w i chosen from it, the task asks the model to predict a binary label @xmath1, literal}. in this section, we propose an auto-augmented structure-aware generative model (aaas) for metaphor detection. firstly, we design an auto-augmented mechanism based on bert @xcite to improve the model's performance when available data is limited. as for the main architecture of the model, we use a typical generative model, bart @xcite , as our backbone, while the other generative models can also accommodate our architecture. particularly, we design a decoder containing the pointer network @xcite and the decoder of bart because the specially designed keywordsextraction task needs the words from the original sentence to construct the structural information. ## experiments compared with existing models, we tried aaas on several metaphor detection tasks. experimental results demonstrate that aaas consistently achieves strong performance on all datasets, which outperforms all of the state-of-the-art baselines methods in terms of accuracy and f1-score. in this section, we attempt to answer the following questions: rq1: does aaas perform better than existing methods? rq2: is aaas still excellent while removing its auto-augmented mechanism? rq3: how do the pattern, j, k, and γ affect the results? algorithm 1: searching algorithm input: train set d train , validation set d val , pre-trained generative model f (•; θ; pattern), auto-augmented method g(•; j, k), maximum tolerance j, and maximum sampling number k. foreach pattern do train the model on d train and update θ using adam. get f1-score on d val . end get the best pattern template for highest f1-score. for @xmath2, 1, ..., j do for @xmath3, ..., k do expand d train to get d expand @xmath4; j, k). train the model f (•; θ; template) on d expand train and update θ using adam. get f1-score on d val . end end get the best values j best and k best of j and k for highest f1-score. return template, j best , k best ## conclusion this paper summarizes existing metaphor detection methods, indicating that almost all of them are discriminant models and rely on external corpora and tools. we propose a structure-aware generative model with an auto-augmented mechanism to solve the problems. we conduct massive experiments on several metaphor detection datasets and achieve remarkable performance. the experimental results demonstrate the effectiveness of the gener-ative method for capturing sequential relationships and interrelationships and the auto-augmented approach for solving the problem of insufficient data. we expect our work will direct more scholarly attention to generative models for metaphor detection and data auto-augmentation methods elaborately designed for insufficient labeled data.