Challenging Assumptions in Learning Generic Text Style Embeddings

• URL: http://arxiv.org/abs/2501.16073v1
• Date: Mon, 27 Jan 2025 14:21:34 GMT
• Title: Challenging Assumptions in Learning Generic Text Style Embeddings
• Authors: Phil Ostheimer, Marius Kloft, Sophie Fellenz,
• Abstract summary: This study addresses the gap by creating generic, sentence-level style embeddings crucial for style-centric tasks. Our approach is grounded on the premise that low-level text style changes can compose any high-level style.
• Score: 24.64611983641699
• License:
• Abstract: Recent advancements in language representation learning primarily emphasize language modeling for deriving meaningful representations, often neglecting style-specific considerations. This study addresses this gap by creating generic, sentence-level style embeddings crucial for style-centric tasks. Our approach is grounded on the premise that low-level text style changes can compose any high-level style. We hypothesize that applying this concept to representation learning enables the development of versatile text style embeddings. By fine-tuning a general-purpose text encoder using contrastive learning and standard cross-entropy loss, we aim to capture these low-level style shifts, anticipating that they offer insights applicable to high-level text styles. The outcomes prompt us to reconsider the underlying assumptions as the results do not always show that the learned style representations capture high-level text styles.

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