Adversarial Multi-task Learning for End-to-end Metaphor Detection (2023.findings-acl)
Copied to clipboard
| 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. |
Similar Papers
End-to-End Sequential Metaphor Identification Inspired by Linguistic Theories (P19-1)
Copied to clipboard
| 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. |
A Match Made in Heaven: A Multi-task Framework for Hyperbole and Metaphor Detection (2023.findings-acl)
Copied to clipboard
| Challenge: | Existing approaches to detect metaphor and hyperbole independently have not explored their relationship computationally. |
| Approach: | They propose a multi-task deep learning framework to detect hyperbole and metaphor simultaneously by annotating two hyperbolic datasets with metaphor labels. |
| Outcome: | The proposed framework improves state-of-the-art hyperbole detection by 12% over existing methods. |
MetaPro 2.0: Computational Metaphor Processing on the Effectiveness of Anomalous Language Modeling (2024.findings-acl)
Copied to clipboard
| 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. |
ContrastWSD: Enhancing Metaphor Detection with Word Sense Disambiguation Following the Metaphor Identification Procedure (2024.lrec-main)
Copied to clipboard
| 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 with Context Enhancement and Curriculum Learning (2024.naacl-long)
Copied to clipboard
| Challenge: | Metaphor detection is a challenging task for natural language processing systems . previous work failed to adequately utilize internal and external semantic relationships . |
| Approach: | They propose a model that leverages the difference between literal and external meanings of words and sentences as the sentence external difference. |
| Outcome: | The proposed model achieves competitive performance across multiple datasets with improved convergence speed compared to other models. |
Metaphorical Polysemy Detection: Conventional Metaphor Meets Word Sense Disambiguation (2022.coling-1)
Copied to clipboard
| Challenge: | Linguists distinguish between novel and conventional metaphors, a distinction which the metaphor detection task in NLP does not take into account. |
| Approach: | They propose a method which treats conventional metaphors as a property of word senses in a lexicon and combines metaphor detection with word sense disambiguation to train it. |
| Outcome: | The proposed model outperforms a state-of-the-art model in annotating metaphor in two subsets of WordNet and achieves .78 ROC-AUC score compared to baseline model . |
Reinforcement Guided Multi-Task Learning Framework for Low-Resource Stereotype Detection (2022.acl-long)
Copied to clipboard
| Challenge: | Existing ‘Stereotype Detection’ datasets adopt a diagnostic approach toward large PLMs. |
| Approach: | They propose a multi-task model that leverages the abundance of data-rich neighboring tasks such as hate speech detection, offensive language detection, misogyny detection, etc., to improve the empirical performance. |
| Outcome: | The proposed model achieves significant gains over baselines on hate speech detection, offensive language detection, misogyny detection, etc. |
Word Embedding and WordNet Based Metaphor Identification and Interpretation (P18-1)
Copied to clipboard
| 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. |
DAGS: A Dependency-Based Dual-Attention and Global Semantic Improvement Framework for Metaphor Recognition (2025.findings-acl)
Copied to clipboard
| Challenge: | Existing methods for metaphor recognition ignore interference caused by literal annotations . et al., 2018: Metaphor recognition plays an important role in cognition and communication . |
| Approach: | They propose a dependency-based Dual-Attention and Global Semantic Improvement framework to improve metaphor recognition. |
| Outcome: | The proposed framework can extract features from multiple information sources while improving on mainstream metaphor datasets. |
Construction Artifacts in Metaphor Identification Datasets (2023.emnlp-main)
Copied to clipboard
| 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. |