Papers with style classification tasks

2 papers
StyLEx: Explaining Style Using Human Lexical Annotations (2023.eacl-main)

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Challenge: Large pre-trained language models often learn spurious domain-specific words to make predictions.
Approach: They propose a model that learns from human annotated explanations of stylistic features and jointly predicts them as model explanations.
Outcome: The proposed model can provide human like stylistic lexical explanations without sacrificing performance on in-domain and out-of-domain datasets.
Meta-Tuning LLMs to Leverage Lexical Knowledge for Generalizable Language Style Understanding (2024.acl-long)

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Challenge: Existing large language models struggle to capture some language styles without fine-tuning.
Approach: They propose to meta-trained LLMs based on representative lexicons to recognize new styles they have not been fine-tuned on.
Outcome: The proposed method improves zero-shot transfer across styles on 13 established and 63 novel tasks generated with LLMs.

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