| Challenge: | Existing models focus on limited forms of linguistic context, such as unigrams. |
| Approach: | They propose end-to-end neural models for detecting metaphorical word use in context . they show that bi-directional biLSTM models which operate on complete sentences work well . |
| Outcome: | The proposed models show that they can learn rich contextual word representations . they are compared to previous models which focused on limited linguistic context . |
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| Challenge: | a new method for metaphor detection uses text from visual datasets to identify words . a metaphor is a complex interaction between two terms, creating an "implicationcomplex" |
| Approach: | They propose a technique for sampling text from visual datasets to create a visibility word embedding. |
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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. |
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories (P19-1)
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| Challenge: | Existing sequence tagging models do not explicitly exploit linguistic theories of metaphor identification. |
| Approach: | They propose to exploit linguistic theories of metaphor identification in deep neural networks to improve model performance. |
| Outcome: | The proposed models achieve state-of-the-art in end-to-end metaphor identification on three datasets. |
Recent advances in neural metaphor processing: A linguistic, cognitive and social perspective (2021.naacl-main)
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| Challenge: | Metaphor processing systems have benefited from recent studies on the role of metaphor in communication and deep learning for natural language processing. |
| Approach: | They present a review of automated metaphor processing and discuss their results from downstream NLP tasks. |
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Metaphors in Pre-Trained Language Models: Probing and Generalization Across Datasets and Languages (2022.acl-long)
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| Challenge: | Existing studies on pre-trained language models assume they encode metaphorical knowledge useful for NLP systems. |
| Approach: | They propose to probing metaphoricity information in PLMs and measure their generalization . they find that contextual representations in PMLs encode metaphorical knowledge . |
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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. |
Learning Outside the Box: Discourse-level Features Improve Metaphor Identification (N19-1)
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| Challenge: | Current approaches to metaphor identification use restricted linguistic contexts, e.g. by only considering a verb’s arguments or the sentence containing a phrase. |
| Approach: | They propose to train simple gradient boosting classifiers on representations of an utterance and its surrounding discourse learned with a variety of document embedding methods. |
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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. |
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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. |
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On the Impact of Temporal Representations on Metaphor Detection (2022.lrec-1)
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| Challenge: | State-of-the-art approaches for metaphor detection compare their literal - or core - meaning and their contextual meaning using neural networks. |
| Approach: | They propose to use temporal and static word embeddings to account for different representations of literal meanings to examine metaphor detection tasks. |
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