Figurative Language Processing: A Linguistically Informed Feature Analysis of the Behavior of Language Models and Humans (2023.findings-acl)
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| Challenge: | Recent years have witnessed a growing interest in investigating what Transformer-based language models (TLMs) actually learn from training data. |
| Approach: | They propose to use a black-box TLM and two intrinsically transparent white-box models to investigate the performance of figurative language models on sarcasm, similes, idioms, and metaphors. |
| Outcome: | The proposed models perform better than other models on figurative language classification tasks. |
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| Challenge: | Figures of speech and figures of language are used in everyday communication . however, this imaginative use of words requires a solid understanding of semantics and real-world knowledge. |
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Investigating Robustness of Dialog Models to Popular Figurative Language Constructs (2021.emnlp-main)
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| Challenge: | Neural language models (NLMs) encode lexical relations and syntactic structure, but their effectiveness is still unclear. |
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IRFL: Image Recognition of Figurative Language (2023.findings-emnlp)
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| Challenge: | Figures of speech are ubiquitous in many forms of discourse, allowing people to convey complex, abstract ideas and evoke emotion. |
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