Challenge: Existing methods for metaphor detection take little consideration on linguistic theories of metaphor detection.
Approach: They propose two BERT-based models for metaphor detection based on examples and definitions of words from the Oxford Dictionary.
Outcome: The proposed models achieve state-of-the-art performance on two established metaphor datasets and are highly interpretable.

Similar Papers

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.
EmbodiedBERT: Cognitively Informed Metaphor Detection Incorporating Sensorimotor Information (2024.findings-emnlp)

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Challenge: Existing methods for metaphor detection rely on heuristics such as Metaphor Identification Procedure (MIP) and Selection Preference Violation (SPV).
Approach: They propose a cognitively motivated module that leverages the cognitive information of embodiment that can be derived from word embeddings and explicitly models the process of sensorimotor change that has been demonstrated as essential for metaphor processing.
Outcome: The proposed module can improve metaphor detection compared with the heuristic MIP that has been applied previously.
MelBERT: Metaphor Detection via Contextualized Late Interaction using Metaphorical Identification Theories (2021.naacl-main)

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Challenge: Existing studies have developed computational models to recognize metaphorical words in sentences.
Approach: They propose a model that leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical.
Outcome: The proposed model outperforms baseline models on four benchmark datasets . it leverages contextualized word representation and linguistic metaphor identification theories to detect whether the target word is metaphorical.
Metaphor Detection via Explicit Basic Meanings Modelling (2023.acl-short)

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Challenge: Existing methods for metaphor detection use the aggregated meaning of a word to approximate its basic meaning.
Approach: They propose a method which models the basic meaning of a word based on literal annotations and compares this with the contextual meaning in a target sentence to identify metaphors.
Outcome: The proposed method outperforms the state-of-the-art method significantly in the F1 score and even reaches the theoretical upper bound on the VUA18 benchmark.
ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure (2024.lrec-main)

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Challenge: Existing methods for identifying metaphoric expressions in text relied on manual effort to identify the basic and contextual meanings of words.
Approach: They propose a model that integrates the Metaphor Identification Procedure (MIP) and Word Sense Disambiguation (WSD) to extract and contrast the contextual meaning with the basic meaning of a word to determine whether it is used metaphorically in a sentence.
Outcome: The proposed model outperforms methods that rely on embeddings or integrate only basic definitions and other external knowledge.
Metaphor Detection via Linguistics Enhanced Siamese Network (2022.coling-1)

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Challenge: Empirical results indicate that MisNet achieves competitive performance on several datasets.
Approach: They propose a model that converts linguistic rules into semantic matching tasks.
Outcome: Empirical results show that MisNet achieves competitive performance on several datasets.
Verb Metaphor Detection via Contextual Relation Learning (2021.acl-long)

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Challenge: Recent work on verb metaphor detection focuses on analyzing restricted forms of linguistic context.
Approach: They propose a model which explicitly models the relation between a verb and its various contexts.
Outcome: The proposed model gets competitive results compared with state-of-the-art approaches on the VUA, MOH-X and TroFi datasets.
Metaphor Detection with Effective Context Denoising (2023.eacl-main)

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Challenge: Existing models focus on semantically relevant information and provide a target-oriented parse tree structure for metaphor detection.
Approach: They propose a new model which introduces a target-oriented parse tree structure for metaphor detection.
Outcome: The proposed model achieves state-of-the-art on several main metaphor datasets and compares with other methods.
Construction Artifacts in Metaphor Identification Datasets (2023.emnlp-main)

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Challenge: Existing metaphor identification datasets can be gamed by completely ignoring the potential metaphorical expression or the context in which it occurs.
Approach: They show that existing metaphor identification datasets can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs.
Outcome: The proposed system can be gamed by fully ignoring the potential metaphorical expression or the context in which it occurs.
It’s Better to Teach Fishing than Giving a Fish: An Auto-Augmented Structure-aware Generative Model for Metaphor Detection (2022.findings-emnlp)

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Challenge: Existing methods to identify metaphors use contextual information extracted by transformers for classifications directly.
Approach: They propose to use structure information extraction to transform the classification task into a keywords-extraction task and to use it to expand the limited datasets.
Outcome: The proposed model obtains competitive results compared with state-of-the-art methods .

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