Modeling Highlighting of Metaphors in Multitask Contrastive Learning Paradigms (2023)

Abstract: Metaphorical language, such as “spending time together”, projects meaning from a source domain (here, \textit{money} ) to a target domain ( \textit{time} ). Thereby, it highlights certain aspects of the target domain, such as the \textit{effort} behind the time investment. Highlighting aspects with metaphors (while hiding others) bridges the two domains and is the core of metaphorical meaning construction. For metaphor interpretation, linguistic theories stress that identifying the highlighted aspects is important for a better understanding of metaphors. However, metaphor research in NLP has not yet dealt with the phenomenon of highlighting. In this paper, we introduce the task of identifying the main aspect highlighted in a metaphorical sentence. Given the inherent interaction of source domains and highlighted aspects, we propose two multitask approaches - a joint learning approach and a continual learning approach - based on a finetuned contrastive learning model to jointly predict highlighted aspects and source domains. We further investigate whether (predicted) information about a source domain leads to better performance in predicting the highlighted aspects, and vice versa. Our experiments on an existing corpus suggest that, with the corresponding information, the performance to predict the other improves in terms of model accuracy in predicting highlighted aspects and source domains notably compared to the single-task baselines.

## introduction a metaphor can be defined as a cross-domain conceptual mapping from a source domain to a target domain @xcite . the abundance in which metaphors occur in everyday language @xcite , such as "winning someone's heart" or "tax evasion", presents a need for metaphor interpretation and, to that end, the computational decoding of metaphors. much like in other forms of figurative language, such as sarcasm, a central challenge with metaphors is that the implicit intended meaning differs from the meaning of the explicit metaphorical expression @xcite . the context provided by the neighboring words in the sentence is an indicator of this implicit meaning @xcite . however, for better comprehending the meaning manifested by a metaphor in a given context, further levels of understanding are needed; for example, information about the source and target domain @xcite @xcite . consider the following metaphorical sentence: here, the word "killing" is used as a metaphor, since business as a concept cannot be killed by tax in physical realms. the intended meaning is manifested by establishing the mapping of two conceptual domains, drawing the meaning from the source domain (physical harm) and projecting the meaning into the target domain (taxation). in this metaphorical context, the word killing is the literal representative of the metaphorical meaning construction. for simplicitly, we henceforth refer to this word as the literal metaphor. according to past research on metaphors @xcite @xcite , the core idea of a metaphor is to highlight certain aspects of its target domain while hiding others; both aspects have largely been disregarded so far in nlp. the more apparent and deliberate of these is highlighting. in the example above, a highlighted aspect may be threat, since the tax poses a threat in terms of the economic distress it can cause. our research builds on the distinction between source and target domains in line with existing research on computational metaphor interpretation @xcite . beyond prior work, however, we argue based on aforementioned linguistic theories that it is also important to have an understanding of the aspects highlighted by a metaphor in a given context for better metaphor interpretation. to fill this gap, we provide the following contributions in the paper at hand: given the corpus of @xcite with metaphorical sentences annotated for highlighted aspects and source domains, we evaluate different variations of our approaches against a single-task baseline for both labels. our results indicate that, in almost all cases, the continual learning approach outperforms a single-task setup, indicating that the combined information of source domains and highlighted aspects benefit the models to predict either of them. our analysis of the results suggests that continual learning particularly learns to differentiate between single source domains and a composite source domains well. ## related work highlighted and hidden aspects of metaphors contribute to the implicit intention conveyed by the metaphor in the given context. as we discuss in the following, different past research has utilized the source and the target domains in the computational analysis of metaphors. to the best of our knowledge, however, no one has investigated how aspects are highlighted by metaphors yet. @xcite defined an ideal metaphor processing system for nlp applications to consist of two components: metaphor detection and metaphor interpretation. in line with their work, @xcite previously identified metaphors to then model metaphor interpretation as a paraphrasing task @xcite . similarly, @xcite used wordnet @xcite to explore the contextual domains of metaphors to then successively identify and interpret metaphors as a use case for machine translation. the authors also design the interpretation step as a paraphrasing task. the majority of computational research on metaphors in natural language has focused on metaphor identification so far, where the task is usually treated as a binary classification task: given an input word or sentence, decide whether the meaning it represents is metaphorical or literal @xcite . one of the pioneering works approached the identification with unsupervised spectral clustering techniques based on relevant parts of speech, such as nouns and verbs @xcite . using a seed phrase to learn similar metaphors associated with particular source domains, the authors adapt the conceptual mapping from one domain to the other. more recent approaches include that of @xcite which employs framenet @xcite to identify metaphors where a roberta-based pretrained language model @xcite is finetuned to encode contextual cues of the concepts original embedding space metaphorical sentence (a) (b) highlighted aspect source domain contrastive learning learned embedding space metaphorical sentence source domain highlighted aspect (a) in the original embedding space, the similarity of the sentence to the correct domain and aspect may be not be high enough. (b) in the learned space, their similarity is increased, and it is decreased for others. we exploit the intrinsic semantic similarity of source domain and highlighted aspect through multitask learning. in the data from framenet for enhanced model performance. first they obtain a sentence representation with one encoder. next, they employ a separate encoder to form framenet based representations of the concepts associated with the given literal metaphor. finally they combine them with the sentence representations to obtain a joint representation, which is passed through a softmax layer for the final prediction. unlike all these works, our research targets the broader interpretation of metaphors by exploiting components of metaphorical meaning construction, like source domains and highlighted aspects. based on the founding work of @xcite previous work in metaphor interpretation has explored the source and target domains with the usage of framenet @xcite and in prediction of source domains @xcite . in the latter, they have proposed to predict source domains in given metaphorical sentences using contrastive learning @xcite . we incorporate their contrastive learning idea in our approach and evaluate it on the same metaphor corpus @xcite . however, not only is our main goal to predict highlighted aspects, but we also exploit the semantic connection between source domains and highlighted aspects using multitask learning. ## approach in this section, we present how to predict both source domains and highlighted aspects jointly with multitask learning. we start from our contrastive learning approach from previous work that we shortly summarize in the following subsection. then, we propose two alternative multitask schemes for the prediction on this basis: continual learning and joint learning. a comparison of the two variants is shown in figure 1 . ## data for our experiments, we employ the corpus of @xcite , which is to our knowledge the only metaphor corpus so far that is annotated for both source domains and highlighted aspects. in the following, we briefly summarize its characteristics and the dataset splits we use. ## experiments this section describes setup of the experiments we carried out on the data from section 4 to study the effectiveness of the two proposed multitask learning approaches from section 3. we present our basic experimental setup, before we give details on the single-task baseline that we compare to as well as on the two approaches. 1 task input a limitation of the approach of sengupta et al. ( 2022 ) is that it appends the literal metaphor annotated in the corpus of @xcite to the input metaphorical sentence (with a separator @xmath0. in a real-world setting this information may not be available. in our experiments, we predict source domains and highlighted aspects without the literal metaphor as input. however, to see the effectiveness of our approaches, we also report on the results with the literal metaphor, with the corresponding single-task baseline setups. baseline we compare our multitask approaches to a single-task approach, namely we use the contrastive learning approach of @xcite here, trained separately for each task. in this setup, during training, the input sentence is first provided to the encoder in order to obtain the input sentence representation. similarly, the corresponding label (highlighted aspect or source domain depending on the downstream task) is provided as an input to the same encoder to get the label representations -and hence the corresponding weights are shared. after that via contrasting learning, the input sentence representation is compared to all the incorrect label representations with paired cosine distance where the training procedure is optimized with multiple negatives ranking loss -which learns the embedding space of the representations such that the given input sentence and the correct label are situated in close proximity in the embedding space. at inference, the model receives an input sentence in the encoder and forms the representation as stated before. then it compares the sentence representation with all the labels present in the corpus for the corresponding downstream task, with paired cosine distance, and ranks all the labels in the order of their similarity. finally, the top-ranked (most similar) label is chosen to be the prediction. for our experiments with the metaphor appended to the input, we perform the training and inference similarly with the literal metaphor appended to the input sentence with a with a separator @xmath1. contrastive learning within the contrastive learning setting, we use deberta @xcite , an enhanced encoder built on top of roberta @xcite , which relies on a disentangled attention mechanism and has shown success in recent nlp research @xcite . we employ it as the encoder for sentence-transformers in all evaluated model configurations. ## results the main results of our experiments are shown in the limited performance of the joint learning approach may speak for that learning on two tasks concurrently confused the model in some cases rather than helping it to learn both tasks better. the following subsections discuss our results in detail. ## analysis to further investigate into our results we looked into the predictions of the two tasks for every experimental setup. in order to do that, we obtained the confusion matrices of the outcomes of our experiments (provided in the appendix) and observed particularly which labels have an improvement resulting from our continual learning approach. we also looked into the cases where both singletask baseline and continual learning fail to predict the correct outcome, to have a better idea where the approach can be improved further. we classify our analysis in two parts: ## conclusion the conceptual mapping from source to target domain in metaphorical meaning manifestation involves the highlighting of certain aspects of the target domain. in this study, we have examined the impact of source domain information on predicting highlighted aspects, and vice versa. to accomplish this, we have proposed two multitask learning approaches within a contrastive learning setting: one utilizing continual learning, the other joint learning. we have evaluated the performance of our approaches in comparison to according single-task baselines for predicting either source domains or highlighted aspects. we have found that continual learning enhances model performance for highlighted aspects and source domains, suggesting mutual improvement. our fine-grained qualitative analysis further confirms the effectiveness of our approaches across various experimental setups. moreover, in a more informal yet potentially more applicable assessment, our method outperforms the performance of the single-task baseline, demonstrating beneficial outcomes through joint learning. we conclude that the aspects highlighted by metaphors can be predicted well in the majority of cases-and even more so a small set of candidate aspects (as suggested by our top-5 accuracy results). we see this as a substantial step towards more comprehensive computational interpretation of metaphors. following @xcite , metaphors do not only highlight certain aspects of a target domain, they also hide others at the same time. future work should thus pay more atten-tion to what is not put emphasis on by a metaphor, which will naturally bring up additional challenges regarding the interpretation of metaphors.