Challenging Assumptions in Learning Generic Text Style Embeddings

TL;DR

This paper investigates the creation of generic sentence-level style embeddings using contrastive learning, challenging assumptions about their ability to capture high-level text styles, and highlighting limitations in current methods.

Contribution

It introduces a novel approach to learning style embeddings by fine-tuning with contrastive learning, questioning the assumption that low-level style changes can represent high-level styles.

Findings

Low-level style shifts may not fully capture high-level text styles.

Contrastive fine-tuning improves style embedding quality.

Results challenge existing assumptions about style representation.

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.