Metaphor Generation with Conceptual Mappings (2021)

Abstract: Generating metaphors is a difficult task as it requires understanding nuanced relationships between abstract concepts. In this paper, we aim to generate a metaphoric sentence given a literal expression by replacing relevant verbs. Guided by conceptual metaphor theory, we propose to control the generation process by encoding conceptual mappings between cognitive domains to generate meaningful metaphoric expressions. To achieve this, we develop two methods: 1) using FrameNet-based embeddings to learn mappings between domains and applying them at the lexical level (CM-Lex), and 2) deriving source/target pairs to train a controlled seq-to-seq generation model (CM-BART). We assess our methods through automatic and human evaluation for basic metaphoricity and conceptual metaphor presence. We show that the unsupervised CM-Lex model is competitive with recent deep learning metaphor generation systems, and CM-BART outperforms all other models both in automatic and human evaluations.

## introduction recent neural models have led to important progress in natural language generation (nlg) tasks. while pre-trained models have facilitated advances in many areas of generation, the field of metaphor generation remains relatively unexplored. moreover, the few existing deep learning models for metaphor generation @xcite @xcite lack any conceptualization of the meaning of the metaphors. this work proposes the first step towards metaphor generation informed by the conceptual metaphor theory (cmt) @xcite @xcite . cmt holds that we use conceptual mappings between domains (conceptual structures that group related concepts) to generate linguistic metaphors. 2 metaphoric mappings consist of a source and a target conceptual domain. the source domain is the conceptual domain from which we draw the metaphorical expressions, while the target domain is the conceptual domain that we try to understand. a classical mapping is argument is war, in which we conceptualize the target argumentation domain as the more concrete source domain of war: we focus on verbs, as they are often the key component of metaphoric expressions @xcite . when used metaphorically, verbs typically evoke source domains (e.g. fought, defended in the above examples): they are concrete, and are used to understand more abstract targets (i.e., argumentation verbs such as argued, supported) via conceptual mappings @xcite . we propose a novel framework for metaphor generation informed by conceptual metaphor theory. given a literal input sentence that evokes a target domain we generate metaphoric sentences that evoke desired corresponding source domain(s). 3 for example, given the literal sentence the party ended as soon as she left evoking the target domain cause to end, we can apply a variety of conceptual mappings to generate different metaphoric outputs evoking different source domains (see figure 1 ). this allows us to generate metaphoric expressions that match known metaphoric mappings, as well as generating from unseen mappings to explore novel metaphors. our contributions are: ## task definition traditional metaphor generation models focus only on whether the generated output is in some way "metaphoric" or not. this ignores the semantic and cognitive properties inherent in metaphoricity. these models can, to some degree, generate metaphors given a literal input, but these outputs often do not evoke the intended metaphor. controlled metaphor generation yields critical benefits over these uncontrolled systems. for sentences in context, having metaphors that are consistent with the text is essential for natural understanding. also, metaphors are not only used to express human knowledge, but can also help shape our understanding of the world: having fine-grained control over the generation process allows us to 3 we note that this source and target terminology used here is opposite to that in machine translation. to achieve controlled metaphor generation, we define our task as follows: given a literal input sentence which evokes a target domain and an intended conceptual mapping, generate a metaphoric sentence such that it evokes a desired source domain. thus, our generation models receive three inputs: 1) a literal input sentence (they argued against the contract), 2) the target domain evoked by the literal input (argument) and 3) the desired source domain (war) for the metaphorical sentence. the output is a metaphorical sentence which evokes the intended mapping (they fought against the contract) ## methods we experiment with two general categories for generation. first, following previous work in metaphor generation and interpretation @xcite , we implement lexical methods for replacement, identifying relevant verbs and replacing them with potential candidates for evoking particular mappings. second, we experiment with deep learning models, employing controlled sequence-to-sequence generation. ## experimental setup we evaluate our metaphor generation methods against two previous approaches to metaphoric paraphrase generation: the mermaid system @xcite and the metaphor masking model (metmask) @xcite . we explore two tasks: generating against gold standard metaphoric expressions, and using rare and unseen metaphoric mappings. for the former, we build a gold test set of metaphoric paraphrases that evoke a particular source/target mapping. for the latter, we apply a variety of source/target mappings to literal inputs for which we do not have gold outputs. ## results and analysis results for automatic evaluation on the 150 gold metaphors are shown in the cm-lex model is competitive with the best neural baseline, which is encouraging. this shows that simply incorporating basic understanding of conceptual mappings can be a powerful tool for metaphor generation. the cm-bart yields the best automatic performance over all metrics, significantly outperforming all other models (@xmath0.01, paired t-test.). automatic metrics allow us to quickly prototype metaphoric generation systems based in conceptual metaphor theory. however, they rely on sbert and inherit the biases and weaknesses therein. we also perform human evaluations, against both the gold test data and the set of rare and unseen mappings. ## related work we broadly cover two areas of related work: previous computational approaches to cmt, and previous approaches to metaphor generation. computational approaches to cmt. there are a variety of approaches to identifying conceptual metaphors themselves. the cormet system @xcite was built to extract conceptual metaphors based on selectional preferences of verbs. @xcite builds "conceptual spaces" for source domains, using rule-based extraction of relations between lexical items. these conceptual spaces are then used to find new conceptual metaphors. this process is extended to build a repository of linguistic and conceptual metaphors @xcite . @xcite focus on identifying appropriate source domains for metaphoric expressions, using vector-based approaches for metaphor interpretation. the idea of using frames to represent metaphoric domains has been explored in the metanet project @xcite . we however, restrict our work to framenet due to the coverage and availability of reliable automatic parsing. metaphor generation. early work in metaphor generation was based in heuristics, learning to generate relatively simple "a is like b" representations @xcite . in a similar vein, veale (2016) uses template-like structures to generate creative and metaphoric tweets. other works focus on identifying metaphoric mappings using wordnet clustering and selectional preferences @xcite , syntactic relations to build proposition databases @xcite , and embedding based approaches to identify poetic relationships @xcite . however, the goal of these works is to generate mappings, rather than linguistic expressions that evoke them. amongst deep learning approaches yu and wan (2019) identify literal and metaphoric words in corpora based on selectional restrictions, and using these to train sequence-to-sequence models for metaphor generation, albeit without reference to any input expression. @xcite generates metaphors using masked language modeling, masking metaphoric tokens in training in order to encourage metaphoric generation. other approaches use novel methods for collecting literal/metaphor pairs, training sequence-to-sequence models for simile generation and metaphoric paraphrasing @xcite . these approaches effectively generate figurative language, but the models have no knowledge of the underlying metaphors, and thus simply generate ungrounded expressions. this leads to outputs which are possibly metaphoric, but contain no connection to the input, eschewing the critical connections that make novel metaphors powerful. we instead propose methods for generating metaphoric para-phrases grounded in cmt. ## conclusions and future work in summary, we have shown two methods for incorporating knowledge of conceptual metaphor theory in metaphor generation. we trained framenet frame embeddings to represent conceptual domains, and applied shifts between them to generate metaphors in an unsupervised fashion. leveraging framenet further, we build a dataset of semisupervised pairs that evoke conceptual metaphors, which can be used along with bart for controlled metaphor generation. this model achieves stateof-the-art performance in metaphor generation by both automatic and human evaluations. future work can expand these models to go beyond verbs, incorporating nominal and other types of metaphors. the next necessary step is to go beyond lexicalized metaphors: good, consistent conceptual metaphors often span long stretches of text, and we need to design models that can learn and generate metaphors over larger texts.