MultiCMET: A Novel Chinese Benchmark for Understanding Multimodal Metaphor (2023.findings-emnlp)
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| Challenge: | Existing research on multimodal metaphors does not address categorizing the source and target domains in metaphors beyond the English language. |
| Approach: | They propose a Cascading Domain Knowledge Integration benchmark to detect metaphors by introducing domain-specific lexical features. |
| Outcome: | The proposed dataset includes 13,820 text-image pairs of advertisements with manual annotations of the occurrence of metaphors, domain categories, and sentiments metaphors convey. |
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| Challenge: | Metaphor is a linguistic phenomenon and a cognitive phenomenon structuring human thought, authors say . previous studies focused on texts, partly due to the unavailability of ground truth labels of multimodal metaphor . |
| Approach: | They propose a multimodal metaphor dataset that integrates multimodal text and image . it contains 10,437 text-image pairs with multimodal annotations of occurrences . |
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Cultural Bias Matters: A Cross-Cultural Benchmark Dataset and Sentiment-Enriched Model for Understanding Multimodal Metaphors (2025.acl-long)
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| Challenge: | Metaphors are pervasive in communication, making them crucial for natural language processing. |
| Approach: | They propose a multicultural multimodal metaphor dataset designed for cross-cultural studies of metaphor in Chinese and English. |
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CMDAG: A Chinese Metaphor Dataset with Annotated Grounds as CoT for Boosting Metaphor Generation (2024.lrec-main)
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| Challenge: | Metaphors are a prominent linguistic device in human language and literature, as they add color, imagery, and emphasis to enhance effective communication. |
| Approach: | They propose a large-scale high quality annotated Chinese Metaphor Corpus . they use a set of guidelines to ensure the accuracy and consistency of their annotations . |
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Metaphor and Large Language Models: When Surface Features Matter More than Deep Understanding (2025.findings-acl)
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| Challenge: | Existing studies on metaphor processing have focused on single datasets and specific task settings, often using artificially constructed data through lexical replacement. |
| Approach: | They propose to evaluate the capabilities of Large Language Models (LLMs) in metaphor interpretation across multiple datasets, tasks, and prompt configurations. |
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The Interplay between Metaphors and NLP (2026.acl-tutorials)
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| Challenge: | This tutorial will provide an overview of the metaphor processing field. |
| Approach: | This tutorial will provide an overview of the metaphor processing field . it will focus on recent directions opened by LLMs for metaphor interpretation . |
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Metaphors in Online Religious Communication: A Detailed Dataset and Cross-Genre Metaphor Detection (2024.lrec-main)
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| Challenge: | figurative language plays a particularly important role in religious communication . linguistic metaphors relate entities from different semantic domains by drawing on an implicit similarity between them. |
| Approach: | They present a dataset of fine-grained metaphor annotations for online religious communication . they show that cross-genre transfer metaphor detection leads to a drop in performance . |
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Chinese Metaphorical Relation Extraction: Dataset and Models (2023.findings-emnlp)
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| Challenge: | Metaphor identification is a core task in metaphor processing, which involves recognizing and analyzing metaphorical expressions in text. |
| Approach: | They propose a new formulation of metaphor identification as a relation extraction problem . they use a dataset to analyze metaphorical relations between two spans, a target and a source . |
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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. |
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ImaRA: An Imaginative Frame Augmented Method for Low-Resource Multimodal Metaphor Detection and Explanation (2025.findings-naacl)
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| Challenge: | Existing methods for multimodal metaphor detection neglect cross-domain and attribute similarity characteristics underlying multimodal understanding. |
| Approach: | They propose an Imaginative FRame Augmented method for multimodal metaphor detection and explanation . they use a cross-modal imagination dataset rich in multimodal multimodal expressions . |
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Adversarial Multi-task Learning for End-to-end Metaphor Detection (2023.findings-acl)
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| Challenge: | Existing methods to learn basic sense discrimination (BSD) are limited in training data. |
| Approach: | They propose a multi-task learning framework to transfer MD knowledge to basic sense discrimination using word sense disambiguation. |
| Outcome: | The proposed framework can mitigate the data scarcity problem in metaphor detection. |