C on F igu R e: Exploring Discourse-level C hinese Figures of Speech (2022)

Abstract: Figures of speech, such as metaphor and irony, are ubiquitous in literature works and colloquial conversations. This poses great challenge for natural language understanding since figures of speech usually deviate from their ostensible meanings to express deeper semantic implications. Previous research lays emphasis on the literary aspect of figures and seldom provide a comprehensive exploration from a view of computational linguistics. In this paper, we first propose the concept of figurative unit, which is the carrier of a figure. Then we select 12 types of figures commonly used in Chinese, and build a Chinese corpus for Contextualized Figure Recognition (ConFiguRe). Different from previous token-level or sentence-level counterparts, ConFiguRe aims at extracting a figurative unit from discourse-level context, and classifying the figurative unit into the right figure type. On ConFiguRe, three tasks, i.e., figure extraction, figure type classification and figure recognition, are designed and the state-of-the-art techniques are utilized to implement the benchmarks. We conduct thorough experiments and show that all three tasks are challenging for existing models, thus requiring further research. Our dataset and code are publicly available at https://github.com/pku-tangent/ConFiguRe .

## introduction figures of speech, also known as rhetoric figures or figurative languages, are a ubiquitous part of spoken and written discourse. these rhetorical techniques, such as metaphor, irony and parallelism, greatly enrich the expression of human languages @xcite . they intentionally deviate from the literal meaning of language to provide deeper semantic expressiveness, therefore discourse fragment: {…} @xcite 1] 这样的想了一想，[2] 我的断绝了联络的知觉，[3] 又重新恢复了转来， {[4] 一股同蒸气似的酸泪，} metaphor/simile(比喻) [5] 直涌了出来。[6] 我踉跄往后退了几步，[7] 倒在 外床她叠好在那里的那条被上。[8] 两手紧紧抱着 了这一条被，{[9] 我哭着，[10] 哭着，[11] 哭着， posing a big challenge to natural language understanding. linguists have a long history of studying rhetoric figures, extensively analyzing their use in literature, culture and psychology @xcite @xcite @xcite . these works mainly focus on collecting qualitative evidence. figures of speech have also drawn attention from the nlp community. many downstream applications could be improved, should figures be precisely identified and carefully dealt with. for example, a faithful translation should adapt the metaphors used in the source language to an authentic expression in the target language, and sentiment analysis should benefit from the correct identification of irony and sarcasm. despite its significance, a comprehensive study of identifying rhetoric figures from discourse remains under-explored. previous works mainly emphasize specific figures, such as metaphor @xcite @xcite and sarcasm @xcite @xcite , or identify rhetoric figures at a token or sentence level @xcite . however, in real-world settings, figurative languages are hidden in a long context and the potential figure type is usually unknown. motivated by this, we construct a comprehensive dataset of 12 commonly used figures in chinese, and include discourse fragment as context for each instance. first we describe our guideline towards constructing a rhetoric corpus, which is devised upon linguistic theory and existing reading practice. the design of the guideline is oriented with two key questions: (1) what is the language carrier of a specific figure ? (2) which figures should be included in our corpus? for the first question, we propose the concept of figurative unit--the smallest continuous clause sequence containing a complete expression of a specific figure. for the second question, we approach it from a linguistic view. linguistically, figures can be divided into two main groups: schemes and tropes. schemes reflect a deviation from the ordinary pattern or arrangement of words, while tropes involve deviation from the ordinary and principal signification of words @xcite . following previous work of chinese linguists @xcite and existing reading-comprehension practice, we select 7 tropes and 5 schemes commonly used in chinese in our work. following the aforementioned guideline, we leverage human annotation to build configure, a chinese corpus for contextualized figure recognition. each instance in configure includes a discourse fragment with several annotated figurative units attached to it. an annotated instance of configure is illustrated in figure 1 . in this instance, the fragment includes 12 chinese clauses and 2 figurative units. clause 4 alone is a figurative unit labeled with the metaphor type, while the latter 4 clauses constitute another figurative unit labeled parallelism. configure is, to the best of our knowledge, the first rhetoric dataset that involves both extracting a figurative unit from the discourse-level context and classifying this unit into the right figure type. in comparison, previous datasets mainly focus on detecting a specific figure from a given sentence @xcite . for benchmark settings, we design three tasks based on configure, namely figure extraction, figure type classification, and figure recognition. we deploy state-of-the-art models as baselines, and reveal that all three tasks remain challenging through thorough experiment. we also conduct subsidiary experiments to explore future directions for these tasks, which will contribute to the research of this area. to sum up, our main contribution is threefold: ## related work over the last decade, automated detection of figurative languages has become a popular topic, and a considerable number of datasets in this area have been constructed. these datasets can be roughly divided into two categories: the first is to extract the span of a specific figure given a sentence and its context (span extraction); the second is to determine whether a sentence is figurative (sentence classification). for span extraction, it is usually accomplished by marking out tokens carrying the target figure. these works include vua @xcite and the ntu irony corpus @xcite . for sentence classification, there are two lines of relevant research. the first is a binary sentence classification task for determining whether a given sentence is figurative, e.g. sarc @xcite for sarcasm, and the chinese rhetoric question dataset built by @xcite . the second is to classify a figurative sentence into its corresponding figure type (multi-classification), such as the chinese multi-label rhetoric dataset constructed by @xcite for joint rhetoric and emotion identification. on the basis of existing datasets, a series of methods have been developed, ranging from feature engineering @xcite @xcite to neural networks @xcite @xcite @xcite @xcite . however, perhaps limited by the fact that existing datasets only target at one figure, previous works mainly deal with specific figure types. a comprehensive study of a collection of figure types remain under-explored. one further observation is that previous datasets are rarely shipped with wider contextual information. they mainly approach the subject from a token-level or sentence-level perspective. that being the situation, a series of works have pointed out that leveraging contextual information is beneficial in figure detection @xcite @xcite @xcite . @xcite proposed to use text incongruity from linguistic theory for sarcasm detection. @xcite used a general and a hierarchical attention mechanism for modeling discourse, improving sota for the 2018 vu amsterdam (vua) metaphor identification shared task @xcite by 6.4 f1-scores. other contextual clues such as author context @xcite , multimodal context @xcite , conversation context @xcite , have also been proven to improve figure detection. in this paper, we propose chinese corpus for contextualized figure recognition (configure), which, to the best of our knowledge, is the first comprehensive corpus that includes more than 10 commonly used figures in chinese, with relevant discourse fragments serving as contextual information for each instance. our dataset can be used for both figure extraction and figure type classification. ## corpus construction previous rhetorical studies @xcite have contributed many useful annotation paradigms. however, these annotation paradigms are primarily customized for one specific figure type, or tailored to scheme figures that present themselves with strict patterns, which are quite inapplicable in our setting. in this section, we present our self-devised annotation guideline to build configure with 7 trope figures and 5 scheme figures. figure 2 : two examples for figurative units. first is a simile unit containing one single clause, the second is a parallelism unit composed of six clauses. ## conclusions and future work in this paper, we argue that it is necessary to recognize figures from the discourse level. in future, we hope to increase the size of our corpus, especially adding more figure instances for the types with fewer instances such as irony and synaesthesia. at the same time, we will to improve the model performance with consideration of incorporating contextual information and using more training data.