What Drives the Use of Metaphorical Language? Negative Insights from Abstractness, Affect, Discourse Coherence and Contextualized Word Representations (2022)

Abstract: Given a specific discourse, which discourse properties trigger the use of metaphorical language, rather than using literal alternatives? For example, what drives people to say grasp the meaning rather than understand the meaning within a specific context? Many NLP approaches to metaphorical language rely on cognitive and (psycho-)linguistic insights and have successfully defined models of discourse coherence, abstractness and affect. In this work, we build five simple models relying on established cognitive and linguistic properties ? frequency, abstractness, affect, discourse coherence and contextualized word representations ? to predict the use of a metaphorical vs. synonymous literal expression in context. By comparing the models? outputs to human judgments, our study indicates that our selected properties are not sufficient to systematically explain metaphorical vs. literal language choices.

## introduction metaphors are "not just nice", but represent a "necessary" element of everyday thought and communication @xcite @xcite , and are ubiquitous in natural language text corpora @xcite @xcite . from the perspective of natural language processing (nlp), automatic approaches to metaphor processing are therefore important for any task that requires natural language understanding, and nlp has been concerned with the detection @xcite @xcite , the interpretation @xcite @xcite and, the generation @xcite of metaphors. 1 as to our knowledge, however, no study so far has raised the question of why a metaphorical expression is used within a specific discourse, rather than an equally plausible literal alternative. for example, consider the discourse in the rich previous interdisciplinary research on figurative language seems to agree on tight interactions between metaphorical language detection and properties of the respective discourses, i.e., cognitive aspects (abstractness and affect), discourse coherence and contextual properties. do these discourse properties indeed trigger the use of a metaphorical vs. its synonymous counterpart? we address this question by exploring five simple discourse-based models inspired by the above-mentioned properties, namely frequency, abstractness, affect, discourse coherence and contextualized word representations. we approach the question within two studies. first, we explore discourse features of metaphorical usages occurring in natural language by applying the cognitive and linguistically-inspired models to existing discourses from the english corpus ukwac @xcite . then, we zoom into the models' predictions and evaluate them against human preferences (i.e., annotations) for metaphorical vs. literal language within the same discourses. by modeling the prediction for synonymous metaphorical vs. literal expressions motivated by the above discourse perspectives, we gain insight into human behavior for metaphorical vs. literal languages choices. ## dataset research on figurative language has produced impressive resources on the metaphoricity of lexical items. however, these resources have limitations regarding the specific task we are addressing in this work: (i) the context in which words are metaphorically used is not large enough, providing human judgments only on the word-level @xcite or on the sentence-level @xcite @xcite , (ii) the target words or expressions are ambiguous, i.e., they may have both a metaphorical and a literal sense @xcite , (iii) they are extended metaphors @xcite , (iv) the paraphrases to metaphorically-used words are automatically generated and no manual annotations were provided to evaluate the outputs @xcite . we therefore use our recently released dataset specifically designed to investigate the choice of metaphorical vs. literal expressions in context (piccirilli and schulte im walde, 2021, 2022). it contains a total of 1,000 discourses of five to six sentences (98 words on average), in which the final sentence of each discourse contains either a metaphorical expression or its literal alternative from a pair of synonymous subject-verb (sv) or verb-object (vo) expressions. ## results and analyses we first analyze the models' predictions with regard to metaphorical vs. literal usages in the originally-extracted data (orig. data), to address the question: do cognitive and linguistically-inspired models reflect metaphorical language usage encountered in natural language corpora? the first column of figure 1 presents the accuracy score of each model with regard to orig. data. all models reach around 50% of accuracy; however, they behave very differently regarding their individual predictions. 3 figure 1 presents the percentages of overlapping output decisions between our five models, revealing interesting insights: we observe 77% overlapping predictions between the plms with the frequency baseline, suggesting that the majority of plm predictions is frequency-driven, which is not the case for most abstractness settings and emotionality. emotionality itself correlates with abstractness in all settings but the one where we consider only adjectives, which is surprising as adjectives tend to carry a lot of emotions @xcite . the overlap between lcg and bert reaches 62%, whereas its correlations with abstractness and emotionality are rather low, suggesting that decisions based on abstractness and emotionality are different to those based on word representations and semantic relatedness. at first sight, our different perspectives seem rather complementary, which is yet to be verified in future studies. overall, none of our models seems to reliably predict what is observed in natural language data. we thus cannot derive what might have triggered the original speakers to favor a metaphorical or literal expression over the other. we then compare our models' predictions to human behavior: are the proportions of metaphorical vs. literal predictions from our models similar to human perceptions (anno. data)? figure 2 presents the proportions of the metaphorical expressions predicted by four of the models in relation to the proportions of metaphorical usages favored by the human judges. 4 if humans and models were making similar decisions, all data points, each representing the proportion of metaphorical uses for a pair of expressions, would be on the regression line. we make different observations depending on the model in question. as far as frequency is concerned, the model predicts either the metaphorical or the literal expression (100% or 0% respectively, in the graph). many literal expressions that are favored by participants (low x-axis %) seem to correlate with their higher occurrences in natural language corpora, e.g., make vs. throw remark (vo). however, humans also favor many metaphorical expressions which are less frequently used, e.g., stir vs. cause excitement (vo) and inversely, a few metaphorical expressions which are more frequent than their literal counterparts are not necessarily favored by people, e.g., poison vs. corrupt mind (vo). thus, frequency does not seem to be a systematic factor for metaphorical/literal choices. regarding abstractness, many expressions fall perfectly on the regression line, e.g., twist vs. misinterpret word (vo), story grab vs. intrigue (sv), suggesting at first some interactions between the expression preferences and the abstractness of the respective discourses. however, the numerous metaphorical expressions that are predicted by the model (80%+) but not by humans, e.g., taste vs. experience freedom (vo), factor shape vs. determine (sv) do not confirm that hypothesis. concerning the lcg, the picture is less clear. many literal expressions are favored by both the model and humans, e.g., color vs. affect judgement (vo), but many metaphorical usages are preferred by humans when the model predicts the literal ones, e.g., breathe vs. instill life (vo). therefore, metaphorical vs. literal coherence does not seem either to be a determining factor for metaphorical vs. literal preference, respectively, which does not support the context-salience hypothesis, where one would expect a metaphorical expression to be favored following a metaphorical discourse, and ditto for a literal expression/literal discourse @xcite . finally, the overall low metaphorical predictions by the plm are expressions for which humans provide high metaphorical proportions, such as abuse vs. drink alcohol (vo). this suggests a potential lack of metaphorical language representation, in line with our observation concerning abstractness and emotionality. ## discussion and future work previous research has provided evidence for interactions between metaphorical language and discourse properties, namely frequency, abstractness, affect, discourse coherence and contextualized word representations, on the word-and sentence-level. in this work, we took a step further: we looked at the task of predicting the use of a metaphorical vs. synonymous literal expression and built models based on the above-mentioned features on the discourse-level. our findings show that these discourse properties do not seem to be indicative of metaphorical usage. we propose several directions for future work. first of all, we considered discourses of around five sentences, but the decision on the context window might have an impact on the findings. further work exploring the optimal size of preceding context would be interesting. another promising direction might analyze plms' attention mechanisms @xcite on the presented task, and also explore the extent to which modifying the attention of such models, i.e., fine-tuning @xcite , improves their performance to mimic human preferences for metaphorical vs. literal usages. finally, we have looked at five features; needless to say that exploring further discourse properties is necessary, such as co-reference, complexity, aptness, creativity, prototypicality, and the influence of genre and specific domains (e.g., religious/scientific texts). ## conclusion we suggested five simple models to investigate why humans choose to use a metaphorical expression in a specific discourse. regardless of the perspectives, our work demonstrates that a range of previously suggested salient discourse properties do not seem to influence preferences on the choice between synonymous metaphorical vs. literal expressions. our findings thus ask for a more nuanced approach to metaphorical language choices in nlp.