Metaphor Detection via Linguistics Enhanced S iamese Network (2022)

Abstract: In this paper we present MisNet, a novel model for word level metaphor detection. MisNet converts two linguistic rules, i.e., Metaphor Identification Procedure (MIP) and Selectional Preference Violation (SPV) into semantic matching tasks. MIP module computes the similarity between the contextual meaning and the basic meaning of a target word. SPV module perceives the incongruity between target words and their contexts. To better represent basic meanings, MisNet utilizes dictionary resources. Empirical results indicate that MisNet achieves competitive performance on several datasets.

## introduction metaphor is an omnipresent figurative language in daily communication. conceptual metaphor theory proposes that metaphor is a mapping mechanism between the source domain and the target domain @xcite . in e.g. 1, the literal meaning of the verb pierce is "some sharp object goes into or on through something" . however, the contextual meaning is "break silence". here, the source domain is a highly abstract action, while the target domain can present the corresponding meaning in a more concrete way. in general, there exists two types of metaphors, i.e., novel metaphors and conventional metaphors. semantic shift theory shows that new lexical senses can derive from metaphors @xcite . once a metaphorical usage of a word is accepted by most people, the metaphorical lexical sense is fixed. thus a polysemant may have metaphorical marginal meanings @xcite . in e.g. 2, the metaphorical target word attack means criticize, which is also a sense of it. it is a conventional metaphor for the metaphorical meaning has been fixed. while e.g. 1 is a novel metaphor, for the metaphorical usage is temporary. linguistic rules instruct us how to identify metaphors. according to metaphor identification procedure (mip) @xcite , a metaphor is identified if the contextual meaning of the target word contrasts with one of its more basic meaning. more basic meanings are: 1) more concrete; what they evoke is easier to imagine, see, hear, feel, smell, and taste; 2) related to bodily action; 3) more precise (as opposed to vague); 4) historically older @xcite . the basic meaning of pierce in e.g. 1 contrasts with its contextual meaning so it is a metaphor. researchers are divided on how to represent basic meanings. @xcite and @xcite used dynamic elmo embeddings and static glove embeddings to encode contextual target meanings and basic target meanings respectively. @xcite proposed that a target used alone is literal. @xcite and @xcite used the gloss (brief definition) of a target to represent its literal meaning. all the methods are not linguistically intuitive, because we do not know whether the basic meaning is accurately encoded through embedding and a basic meaning is not simply the average of its gloss. since basic meanings are not properly represented, mip may be invalid. consequently, conventional metaphors are ignored by previous studies as well @xcite . another linguistic rule is selectional preference violation (spv) @xcite , which suggests that a metaphor is identified by noticing semantic incongruity between a target word and its context. in e.g. 1, pierce rarely occurs in the context consisting of scream and night. there comes a contextual contrast in such a collocation. notice that spv becomes invalid when faced with a conventional metaphor, because the context is also usual for the metaphor word like attack in e.g. 2. to better use linguistic rules, we propose to use the sentence where the target adopts its basic meaning for a better representation. this idea is in line with mainstream language models: you shall know a word by the company it keeps @xcite . in this paper, we propose a novel metaphor detection model named metaphor identification from siamese network (misnet). misnet adopts a siamese framework, consisting of two separate encoders . we regard mip as a representation based semantic matching task between target in the given sentence and target in the basic usage. mip is accomplished across two encoders. we model spv as an interaction based semantic matching task between the target word and its context to measure semantic incongruity. spv is implemented within a single encoder. based on the fusion of mip and spv modules, misnet makes a final prediction. the contributions of this paper can be summarized as follows: it is also proficient in tackling conventional metaphors. ## related work recently, metaphor detection has attracted lots of attention. with the development of nlp technologies, various methods have been applied. in general, these approaches can be categorized into three types: feature engineering based, rnn based and transformer based methods. feature engineering based methods use linguistic features such as word concreteness, word abstractness @xcite , and word class etc., as input of a certain machine learning model like logistic regression and svm @xcite @xcite . rnn based models use rnn as a feature extractor to form contextual representations to identify metaphors @xcite @xcite . though great improvements have been made, rnn based models mostly use static word representations like word2vec and glove, so they are not adept at metaphors which convey complex contextual senses. also, due to the nature of rnn, these models cannot be paralleled. transformer based methods use a pretrained language model (plm) like bert @xcite and roberta @xcite , as the backbone of the model, yielding promising results in metaphor detection. @xcite converted metaphor detection into a machine reading comprehension task with various linguistic features incorporated, achieving best reported results in acl 2020 metaphor detection shared task. @xcite used a late-interaction mechanism to encode the contextual meaning and the literal meaning of a target word. @xcite focused on verb metaphor detection. they used dependency parsing to extract the objects and subjects of the given verbs to use syntactic relations. @xcite utilized contrastive learning to distinguish metaphors from literal usages. they also used selftraining strategy to generate pseudo-labels, which largely expanded existing public datasets. also, some recent researches noticed that external dictionary resources could greatly help metaphor detection. @xcite and @xcite used glosses to interpret target words. they took the average embeddings of a gloss as the corresponding target representation. however, the meaning of a word is not simply the average of its definition. there still leaves much space to better represent literal meanings. ## proposed model some researchers use sequence labeling to detect metaphors, i.e., label all n words in a sentence in one go @xcite . in this paper, we use word classification paradigm to detect metaphors. we regard each word in the given sentence as target in order, then take a target along with its given sentence to predict the metaphoricity of the target for n times. ## conclusion in this paper, we propose a novel metaphor detection model named misnet, which uses mip to compare the discrepancy between contextual target word meaning and its basic meaning, and utilizes spv to measure the incongruity between the target and its context. misnet takes basic usages to encode basic target meanings, which can prevent the invalidation of mip and spv when dealing with conventional metaphors. empirical results show that our method achieves competitive performance on several datasets.