Verbal Multiword Expressions for Identification of Metaphor (2020)

Abstract: Metaphor is a linguistic device in which a concept is expressed by mentioning another. Identifying metaphorical expressions, therefore, requires a non-compositional understanding of semantics. Multiword Expressions (MWEs), on the other hand, are linguistic phenomena with varying degrees of semantic opacity and their identification poses a challenge to computational models. This work is the first attempt at analysing the interplay of metaphor and MWEs processing through the design of a neural architecture whereby classification of metaphors is enhanced by informing the model of the presence of MWEs. To the best of our knowledge, this is the first “MWE-aware” metaphor identification system paving the way for further experiments on the complex interactions of these phenomena. The results and analyses show that this proposed architecture reach state-of-the-art on two different established metaphor datasets.

## introduction human language is rife with a wide range of techniques that facilitate communication and expand the capacities of thinking and argumentation. one phenomenon of such kind is metaphor. metaphor is defined as a figure of speech in which the speaker makes an implicit comparison between seemingly unrelated things which nonetheless have certain common characteristics @xcite . this is done to convey an idea which is otherwise difficult to express succinctly or simply for rhetorical effect. as an example, in the sentence she devoured his novels, the verb devour is used in a metaphorical sense that implies reading quickly and eagerly. the literal and metaphorical senses share the element of intense desire which in turn helps to decode the meaning of the word in its context. it is clear that a mere literal understanding of semantics would not result in proper understanding of a metaphorical expression and a non-compositional approach would be required @xcite . the human brain is equipped with the necessary machinery to decode the intended message behind a metaphorical utterance. this involves mentally linking the seemingly unrelated concepts based on their similarities @xcite . verbal mwes (vmwes) are another example of non-literal language in which multiple words form a single unit of meaning. these two phenomena share some common ground. expressions like take the bull by the horns, go places, kick the bucket, or break someone's heart can be categorised as metaphorical vmwes. based on this observation we hypothesise that a metaphor classification model can be bolstered by knowledge of vmwes. in this work we focus on how identification of verbal metaphors can be helped by verbal mwes. we devise a deep learning model based on attention-guided graph convolutional neural networks (gcns) that encode syntactic dependencies alongside information about the existence of vmwes and we test the model on two established metaphor datasets. ## related works the tasks of mwe and metaphor identification share some similarities. many idiomatic mwes can be considered as lexicalised metaphors. idioms are where the overlap becomes clear @xcite . it is important to note, however, that not all verbal metaphors are vmwes. metaphors that are less conventionalised and appear in creative context (e.g. within a poem or a literary piece) and are not established enough to make it as entries into dictionaries are examples of such cases. however, the distinction between these categories is not always clear, and few precise tests exist for the annotators to tell them apart @xcite . 1 most state-of-the-art mwe identification models are based on neural architectures @xcite with some employing graph-based methods to make use of structured information such as dependency parse trees @xcite . top-performing metaphor detection models also use neural methods @xcite , with some utilising additional data such as sentiment and linguistic information to further improve performance @xcite . ## graph convolutional networks graph convolutional networks (gcns) (kipf and welling, 2016) are a variation of the classic cnns that perform the convolution operation on nodes of a graph, making them suitable for capturing nonsequential inter-dependencies in the input. using the per-sentence formalism @xcite , gcn can be defined as: ## experiments we describe the datasets used in the experiments and then provide details of the overall system. ## results we report the results in terms of accuracy, precision, recall and f 1 -score, macro averaged over the measures obtained from 10 fold cross-validation. as can be seen in in order to have a fair comparison with the previous state-of-the-art, it is important to consider their architectures. @xcite , which our model outperforms in most criteria across the two datasets, is a bilstm-based system that uses a combination of elmo and glove vectors for input representation. the two models by @xcite are more competitive, especially in accuracy and precision for the trofi dataset. rnn-hg and rnn-mhca are bilstm-based systems grounded in linguistic theories of selectional preference violation (spv) @xcite and metaphor identification procedure (mip) @xcite which are based on the semantic contrast between the metaphorical word and its context or between the literal and contextualised meanings of a target token. these two models also make use of contextualised embeddings. 4 an index specifies the location of the target token. ## discussion the larger portion of annotated vmwes in both datasets are figurative and thus provide a valuable signal to metaphoricity. trofi proved to be more challenging as sentences can be as long as 118 tokens with several different vmwes and only a single token of interest which could be labelled as literal. on the other hand, moh-x is more focused and vmwes, for the most part, coincide with the target verb. a notable pattern in the results is when the baselines miss a metaphor and the proposed model correctly identifies it due to the presence of a noncompositional vmwe. a typical example is given below where tack together, identified initially as an mwe, signals metaphoricity: foot_1 (1) he tacked together some verses. there are examples of sentences falsely classified by bert+gcn as metaphorical which are correctly identified as not by bert+mwe-aware gcn. this shows the model has picked up informative cues and general patterns. there are also metaphors missed by bert+gcn that do not have explicitly tagged vmwes, but the proposed model is still able to capture them. example 2 is an instance of such case: due to their correlation with metaphoricity, vmwe information equips the model with the ability to identify metaphorical usage, which is reflected in the superior precision scores. however, this correlation is not always definitive, and in certain cases where a vmwe is realised in its literal meaning, the model might incorrectly associate its presence with metaphor. the following two sentences from moh-x are examples of false positives influenced by vmwes. here, jam the brake and land in are vmwes with literal meanings which can be idiomatic in other contexts: ## conclusions and future work in this work, we presented a neural model to classify metaphorical verbs in their sentential context using information from the dependency parse tree and annotations for verbal multiword expressions. to the best of our knowledge, this is the first mweaware metaphor identification system, that demonstrates how the knowledge of mwes can enhance the performance of a metaphor classification model. experiments showed that the resulting system sets a new state-of-the-art in several criteria across two benchmark metaphor datasets. the code used in the experiments will be made publicly available 6 . for future work, we plan to add vmwe annotations to the vu amsterdam corpus @xcite which is the largest metaphor dataset and extend our experiments using that resource. directionality of edges did not result in improvement in our models in this work, however for future, we plan to develop gcns that incorporate edge typing, which would enable us to differentiate between different mwe types and dependency relations while comparing them against the current models.