Back to the Roots: Predicting the Source Domain of Metaphors using Contrastive Learning (2022)

Abstract: Metaphors frame a given target domain using concepts from another, usually more concrete, source domain. Previous research in NLP has focused on the identification of metaphors and the interpretation of their meaning. In contrast, this paper studies to what extent the source domain can be predicted computationally from a metaphorical text. Given a dataset with metaphorical texts from a finite set of source domains, we propose a contrastive learning approach that ranks source domains by their likelihood of being referred to in a metaphorical text. In experiments, it achieves reasonable performance even for rare source domains, clearly outperforming a classification baseline.

## introduction metaphors foster meaning in language by establishing a mapping between two conceptual domains, where concepts rooted in a usually rather concrete source domain are projected to a usually rather abstract target domain @xcite . in other words, metaphors explain one concept in terms of another concept. for example, in the sentence "the sales tax would generate $12 billion in annual tax revenues", the target domain taxation is described through concepts from the source domain machine, as indicated by the verb "generate". recent research suggests that even state-of-theart nlp models face problems with making inferences on figurative language such as metaphors @xcite . to better comprehend the meaning intended by metaphorical language, additional levels of understanding need to be incorporated. so far, past research in natural language processing has focused on the distinction of literal from metaphorical text @xcite as well as on the interpretation of metaphors in terms of understanding their literal meaning from their intended meaning and vice versa @xcite . for these tasks, the mapping between source and target domain has often been used as an effective cue. to the best of our knowledge, however, no work directly attempts the actual identification of the conceptual domains of metaphors from a given sentence. a reason behind may lie in the theoretical unboundedness of the number of concepts (and, as a result, the space of metaphors) associated with a single concept. in this paper, we study to what extent source domains can be predicted computationally from given metaphorical sentences. we restrict our view to the slightly simplified setting in which a set of possible source domains is predefined (but possibly large). conceptually, this makes the task a classification problem: given the sentence, assign it to the correct source domain. however, for larger numbers of source domains, it may be hard to learn a reliable classification model, particularly when annotated metaphor data is limited. instead, we therefore propose a contrastive learning approach @xcite based on our hypothesis that the source domain and the metaphorical sentences are related linguistically. the approach ranks all source domains based on the similarity of their embeddings to the embedding of the given sentence. at inference time, it then chooses the top-ranked source domain. we evaluate our approach on the corpus of @xcite , covering 1429 english metaphorical sentences and 138 source domains. with an accuracy of 0.619, our approach clearly outperforms transformer-based classification baselines, especially on rare source domains. even though the unboundedness problem remains, we thereby contribute towards a better computational understanding of metaphorical language. to go beyond, we expect that modeling external knowledge about source domains will be needed. ## related work as stated above, past nlp research has tackled the study of metaphors mostly in the form of two sentence metaphor src. domain the sad news is with the exception of very few no firearm organisation is doing anything of the slightest value in fighting gun control. fighting struggle, war this is the historical context of obama's election victory. victory competition, game, war they attack ""rich people"" while enjoying all the spoils of their luck, i have zero problems with earned wealth, but these clowns literally lucked out in life. attack war the author theorized different metaphors in terms of mapped concepts (source and target domains). approaches to metaphor interpretation have particularly witnessed unsupervised extraction of source domains and target domains to interpret the intended meaning of metaphorical expressions @xcite . in contrast, we seek to predict the source domain, even if it is not mentioned in the text. notable research combining metaphor identification and interpretation has been carried out by @xcite . the authors first identified metaphors by verb and noun clustering, followed by interpreting the intended meaning of the metaphors by addressing it as a paraphrasing task. @xcite modeled explicit conceptual metaphors (where the source domain and the target domain are situated as excerpts of text in the sentence) and implicit conceptual metaphors (where the two domains are not apparent), where they extracted source and target domains in an unsupervised approach. a limitation of their work is that no evaluation is provided regarding how authentic the source domains and the target domains are that are excavated. recently @xcite have interpreted metaphors by extracting source and target domains from the semantic space of their associated con- ahrens and jiang (2020) developed an algorithm to identify source domains from text with the help of lexical resources like wordnet, which partially addressed the unboundedness problem of source domains. however, their work is essentially an annotation procedure for source domain verification. the only datasets suitable for our purposes are the one of @xcite , where source and target domains have been annotated manually, and the one of @xcite where both conceptual source and target domains and their linguistic triggers are given. we rely on the latter, since the former one has only 761 samples. ## data to study the task of predicting the source domain of metaphors, we need data where source domains are annotated. we employ the dataset of @xcite , which was originally created to explore how the meaning shift @xcite happens between source and target domains. the dataset contains 1771 metaphorical sentences, spanning 70 source domains annotated for the linguistic metaphors (metaphorical text excerpts in the sentence corresponding to source and target domains). we use the "source linguistic metaphor" and henceforth refer to it simply as metaphor. for example, in the sentence "an invasion of wealth may not suit their interests", the metaphor is "invasion" and the annotated source domain is war. ## approach for a predefined set of domains, we here model source domain prediction as a ranking task. given a metaphorical sentence as input, we rank all candidate domains by their likelihood of being the source domain based on their semantic similarity to the sentence. then, we choose the top-ranked domain as the predicted source domain. to that end, we develop a contrastive learning approach which compares the semantic representations of the input sentence and the candidate domains. figure 2 gives an overview. ## experiments this section reports on first experiments that we carried out to evaluate our approach to source domain prediction against different baselines. the goal was to study whether and when contrastive learning provides advantages over standard classification in the given task. foot_1 ## conclusion understanding a metaphor includes the recognition of the source domain from which concepts are projected to the target domain being discussed. in this paper, we have proposed a contrastive learning approach to recognize the source domain from a given metaphorical text computationally, when the set of domains is predefined. experiments suggest that the approach works reasonably well, particularly for source domains that are represented scarcely, which we expect to likely happen often in real-world situations. however, the obtained results also reveal notable room for improvement. in future work, we plan to investigate the impact of modeling external knowledge about the domains as well as the recognition of source domains in unbounded settings.