Metaphor Detection via Explicit Basic Meanings Modelling (2023)

Abstract: One noticeable trend in metaphor detection is the embrace of linguistic theories such as the metaphor identification procedure (MIP) for model architecture design. While MIP clearly defines that the metaphoricity of a lexical unit is determined based on the contrast between its contextual meaning and its basic meaning, existing work does not strictly follow this principle, typically using the aggregated meaning to approximate the basic meaning of target words. In this paper, we propose a novel metaphor detection method, which models the basic meaning of the word based on literal annotation from the training set, and then compares this with the contextual meaning in a target sentence to identify metaphors. Empirical results show that our method outperforms the state-of-the-art method significantly by 1.0% in F1 score. Moreover, our performance even reaches the theoretical upper bound on the VUA18 benchmark for targets with basic annotations, which demonstrates the importance of modelling basic meanings for metaphor detection.

## introduction metaphors are widely used in daily life for effective communication and vivid description. due to their unusual and creative usage, further processes are required for machines to understand metaphors, which results in computational metaphor processing (cmp), an active research direction in nlp @xcite . recent studies demonstrate that cmp can benefit a wide range of nlp tasks including creative language generation @xcite , sentiment analysis @xcite , and machine translation @xcite . metaphor identification, aiming to detect words used metaphorically, is the very first stage in cmp. for example, target words 'attack' or 'defend' in the context sentence "he attacks/defends her point." do not literally involve physical engagement, so they are supposed to be identified in metaphor detection for further process @xcite . linguists, philosophers and psychologists propose various ways to define metaphors, including substitution view @xcite , comparison view @xcite , class inclusion view @xcite , and conceptual metaphor theory @xcite . in contrast to these theories which are relatively complex in nature, @xcite propose a simple and effective linguistic theory called metaphor identification process (mip) which can identify metaphors in unrestricted textual corpora. mip gains increasing popularity as it detects metaphorical terms regardless of specific conceptual mapping or comparison among source and target domain, which makes the identification operational and straightforward. according to mip, a word is tagged as a metaphor if its contextual meaning contrast with its "more basic meaning". the basic meaning here is defined as "more concrete; related to bodily action; more precise (as opposed to vague); historically older" guided by dictionaries foot_0 . for example, in the sentence "this project is such a headache!", the target headache here is metaphorical since its contextual meaning is "a thing or person that causes worry or trouble; a problem", which contrasts with the more basic meaning "a continuous pain in the head" foot_1 . existing deep learning methods for metaphor identification usually depend on mip in their model design @xcite @xcite @xcite . however, existing works usually ignore basic meaning modelling and instead use aggregated meaning to contrast with contextual meaning in mip. we call the mip in these implementations 'aggregated mip' (amip). for example, @xcite and @xcite implement mip by contrasting contextual meaning representation with glove embedding and decontextualised foot_2 roberta embedding, respectively. however, aggregated meaning representations, such as glove and decontextualised embeddings, are not the same as basic meanings in general. they usually represent a frequencybased weighted average of multiple word meanings. in cases where the basic meaning is the most frequent, then the aggregated meaning can be a reasonable approximation to basic meaning. however, it is very common that metaphorical meanings are more frequent so that using aggregated meaning violates the fundamental rule of mip. for example 'back' means 'the rear surface of the human body' as basic meaning, but its non-basic senses, e.g. 'going back', 'back up', 'back in 1960', are more frequently used in corpora. this makes the aggregated representation of back diverge from its basic sense, so that metaphor cannot be identified via measuring contrast with contextual meaning. a further pitfall of previous works is that the aggregated representations used are static rather than contextualised. for example, aggregated representation glove and decontextualised roberta embeddings used by @xcite and @xcite are both static embedding, which are not compatible with the contextual meaning they compared to and has been shown to have worse representational quality @xcite . in this paper, we propose a novel metaphor identification mechanism, basicmip, which implements mip via direct basic meaning modelling of targets. basicmip explicitly leverages basic annotations from training set, where basic meaning of words are labeled as literal according to mip theory. first, it samples literal instances for each tar-get. then, the basic meaning representation of target is obtained by summing up the target embeddings of sampled literal instances. finally, the basic representations are contrasted with their contextual meaning representation in target sentences to identify metaphors. we also present our novel metaphor detection model, basicbert, which not only uses basicmip but also inherits the amip module and spv @xcite theory from prior works. extensive experiments conducted on two metaphor benchmarks show that basicbert significantly outperforms current sotas. in the vua20 benchmark, our model exceeds melbert by 1% in f1 score. in the vua18 benchmark, our performance even reaches the theoretical upper bound for the targets with literal annotations in the training set. our code and data can be found at https://github.com/ liyucheng09/basicbert . ## method basicbert model consists of three main components: basicmip, amip, and spv. we include both amip and basicmip as some words do not have literal annotations in training set, so amip is an useful augmented component for these cases. ## conclusion we proposed basicbert, a simple but effective approach for metaphor detection. the key feature of our method is the basic meaning modelling for metaphors from training annotations. extensive experiments show that our model achieves best results on two benchmarks against sota baselines and also reaches the theoretical upper bound for instances with basic annotation. we believe our approach can be extended to other creative language with minor updates. in future, we will try apply our approach to identify other types of creative language, such as humour and sarcasm.