| Challenge: | Existing algorithms for suggesting metaphors have been used to find related words . corpus studies have found that metaphors are very pervasive even in formal language . |
| Approach: | They propose an algorithm that suggests metaphoric means of referring to concepts . they use MetaNet, a repository of conceptual metaphor, and lexical resources . |
| Outcome: | The proposed model expands the potential of the original repository by enabling new connections to be drawn. |
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Metaphor Generation with Conceptual Mappings (2021.acl-long)
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| Challenge: | Existing models for metaphor generation lack conceptualization of meaning of the metaphors . recent neural models have led to advances in many areas of natural language generation . |
| Approach: | They propose to encode conceptual mappings between cognitive domains to generate metaphoric expressions by embedding verbs into a literal expression and deriving source/target pairs to train a controlled seq-to-seq generation model. |
| Outcome: | The proposed method outperforms existing models in automatic and human evaluations for basic metaphoricity and conceptual metaphor presence. |
MetaPro Online: A Computational Metaphor Processing Online System (2023.acl-demo)
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| Challenge: | Metaphors do not take literal meanings in contexts, which may cause difficulties for language learners and machines to understand them. |
| Approach: | They propose a computational metaphor processing online system that queries metaphoricity labels, paraphrases and concept mappings for non-domain-specific text. |
| Outcome: | The proposed system can query metaphoricity labels, paraphrases, and concept mappings for non-domain-specific text without coding background. |
Word Embedding and WordNet Based Metaphor Identification and Interpretation (P18-1)
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| Challenge: | Existing models cannot identify exact metaphorical words within a sentence . current models do not rely on hand-crafted knowledge for training . |
| Approach: | They propose an unsupervised learning method that identifies and interprets metaphors at word-level without preprocessing. |
| Outcome: | The proposed method outperforms baseline models in two translation systems for English to Chinese showing that it paraphrases metaphors into their literal counterparts. |
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)
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| Challenge: | Existing methods for metaphor interpretation are slow due to lack of annotated datasets and effective pre-trained language models. |
| Approach: | They propose a large annotated dataset and a PLM for the metaphor interpretation task. |
| Outcome: | The proposed method improves on metaphor identification and interpretation with comparable baselines on the new dataset. |
How to Avoid Sentences Spelling Boring? Towards a Neural Approach to Unsupervised Metaphor Generation (N19-1)
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| Challenge: | Existing approaches to generate metaphors rely on template-based or rule-based knowledge, which constrains the diversity of generated metaphors. |
| Approach: | They propose a neural approach to metaphor generation that uses wiki corpus to extract metaphorically used verbs and train a language model. |
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Automatic Extraction of Metaphoric Analogies from Literary Texts: Task Formulation, Dataset Construction, and Evaluation (2025.coling-main)
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Joanne Boisson, Zara Siddique, Hsuvas Borkakoty, Dimosthenis Antypas, Luis Espinosa Anke, Jose Camacho-Collados
| Challenge: | Recent advances in large language models (LLMs) have shown to be difficult to extract metaphors from free text because they can involve some implicit concepts and link dissimilar concepts. |
| Approach: | They compare the ability of large language models to extract metaphors from literary texts using domain experts. |
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Weeding out Conventionalized Metaphors: A Corpus of Novel Metaphor Annotations (D18-1)
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| Challenge: | a lack of datasets distinguish between conventionalized and novel metaphors is limiting research . a novel metaphor is often overlooked or intentionally disregarded, authors say . |
| Approach: | They propose a crowdsourced annotation layer for an existing metaphor corpus to investigate novelty . they investigate correlations between concreteness ratings and more semantic features . |
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Does GPT-3 Grasp Metaphors? Identifying Metaphor Mappings with Generative Language Models (2023.acl-long)
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| Challenge: | Existing approaches to detect whether natural language sequences are metaphoric or literal focus on detecting the transfer of knowledge structures to pre-trained language models. |
| Approach: | They propose to probe the ability of GPT-3 to detect metaphoric language and predict the metaphor’s source domain without any pre-set domains. |
| Outcome: | The proposed model generates the correct source domain for a new sample with an accuracy of 65.15% in English and 34.65% in Spanish. |
FrameBERT: Conceptual Metaphor Detection with Frame Embedding Learning (2023.eacl-main)
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| Challenge: | Existing models for concept-level metaphor detection lack explicit knowledge of FrameNet . Metaphor detection is a pervasive linguistic device that is used in cognitive and communicative functions of language. |
| Approach: | They propose a BERT-based model that explicitly learns FrameNet Embeddings for metaphor detection. |
| Outcome: | The proposed model is more explainable and interpretable than existing models. |
A Corpus of Metaphor Novelty Scores for Syntactically-Related Word Pairs (L18-1)
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| Challenge: | Existing data on metaphor novelty are limited, making it difficult to perform research on this topic. |
| Approach: | They propose to release a corpus of metaphor novelty scores for syntactically related word pairs . they establish a performance benchmark to which future researchers can compare . |
| Outcome: | The proposed corpus of metaphor novelty scores is compared to other datasets . it performs better than chance or nave strategies, the authors show . |