D eep M et: A Reading Comprehension Paradigm for Token-level Metaphor Detection (2020)

Abstract: Machine metaphor understanding is one of the major topics in NLP. Most of the recent attempts consider it as classification or sequence tagging task. However, few types of research introduce the rich linguistic information into the field of computational metaphor by leveraging powerful pre-training language models. We focus a novel reading comprehension paradigm for solving the token-level metaphor detection task which provides an innovative type of solution for this task. We propose an end-to-end deep metaphor detection model named DeepMet based on this paradigm. The proposed approach encodes the global text context (whole sentence), local text context (sentence fragments), and question (query word) information as well as incorporating two types of part-of-speech (POS) features by making use of the advanced pre-training language model. The experimental results by using several metaphor datasets show that our model achieves competitive results in the second shared task on metaphor detection.

## introduction metaphor is one of the figurative languages and often used to express our thoughts in daily conversations. it is deeply related to human cognitive processes @xcite . metaphor is used to implicitly refer one concept to another concept, usually triggered by a verb @xcite . for example, the verb "drink" in "car drinks gasoline" is a metaphorical usage. other parts of speech can also be used metaphorically @xcite . for example, the noun "angel" in "she is an angel" and the adjective "bright" in "your idea is very bright" are also metaphorical uses. metaphor computation technologies are helpful for most nlp tasks such as machine translation, dialogue systems, content analysis, and machine reading comprehension. of these, token-level metaphor detec-tion is the basic technology for metaphor understanding. its task is to give a text sequence and determine whether a token in the given text sequence is a metaphor or literal. the second shared task on metaphor detection foot_0 aims to promote the development of metaphor detection technology. this task provides two data sets, vu amsterdam metaphor corpus (vua) @xcite and toefi (a subset of ets corpus of non-native written english) @xcite , each with two tasks. each dataset has two tasks, i.e., verb metaphor detection and all pos metaphor detection. previous research @xcite @xcite has been limited to treat them as the text classification task or sequence tagging task without deeply investigating and leveraging the linguistic information that may be proper for the specific metaphor understanding task. motivated by the previous work mentioned in the above, we propose an end-to-end neural based method named deepmet for detecting metaphor by transforming the token-level metaphor detection task into the reading comprehension task. our approach encodes the global text, local text and question information as well as incorporating the pos features on two granularity. to improve the performance further, we also leverage the powerful pre-training language models. the f1 score of our best model reaches 80.4% and 76.9% in the verbal track and the all pos track of the vua data set, and 74.9% and 71.5% in the verbal track and the all pos track of the toefl data set, respectively. our source codes are available online foot_1 . the main contributions of our work can be summarized: (1) we propose a novel reading comprehension paradigm for token-level metaphor detection task. (2) we design a metaphor detec-tion model based on the reading comprehension paradigm which makes use of the advanced pretraining language model to encode global, local, and question information of the text as well as two types of pos auxiliary features. we also introduced a metaphor preference parameter in the cross-validation phase to improve the model performance. (3) the experimental results on several metaphor datasets show that our model is comparable to the state-of-the-art metaphor detection, especially we verified that fine-grained pos (fgpos) features contribute to performance improvement in our model. ## conclusion and future work this paper proposed a reading comprehension paradigm for metaphor detection. according to this reading comprehension paradigm, we designed an end-to-end neural metaphor detector, which processes global and local information of the text through the transformer encoder, and introduces two pos with different granularity as additional features. throughout the experiments on four metaphor detection data sets, we found that the model works well, and a competitive result is achieved good performance in the second metaphor detection sharing task. we also designed ablation experiments to verify the influence factors of the model and found that fine-grained pos and global text information is more helpful to the metaphor detection ability of the model. there are a number of interesting directions for future work: (1) metaphor is a special figurative language and we will extend our research methods to other figurative languages such as metonymy, simile, satire, and pun. (2) we will introduce linguistic theory into our framework to make a deep learning model more explanatory. (3) through error analysis, we find that the multiple words trigger metaphor will affect the performance of the metaphor detection model. we will consider the multi-word question metaphor detection based on the reading comprehension paradigm.