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Challenging Assumptions in Learning Generic Text Style Embeddings

Phil Ostheimer, Marius Kloft, Sophie Fellenz

TL;DR

This work investigates whether generic, sentence‑level text style embeddings can be learned by assuming that low‑level stylistic changes compose high‑level styles. The authors fine‑tune a general text encoder on StylePTB using contrastive and cross‑entropy objectives to produce style‑discriminative representations and evaluate transfer to multiple high‑level style datasets. Results show that cross‑entropy fine‑tuning yields selective improvements, while contrastive learning often degrades performance, challenging the central assumption and indicating that current contrastive setups may misalign with style learning. The study highlights the need for more nuanced training signals and dataset diversity when pursuing generalizable style representations with practical implications for style classification and transfer tasks.

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.

Challenging Assumptions in Learning Generic Text Style Embeddings

TL;DR

This work investigates whether generic, sentence‑level text style embeddings can be learned by assuming that low‑level stylistic changes compose high‑level styles. The authors fine‑tune a general text encoder on StylePTB using contrastive and cross‑entropy objectives to produce style‑discriminative representations and evaluate transfer to multiple high‑level style datasets. Results show that cross‑entropy fine‑tuning yields selective improvements, while contrastive learning often degrades performance, challenging the central assumption and indicating that current contrastive setups may misalign with style learning. The study highlights the need for more nuanced training signals and dataset diversity when pursuing generalizable style representations with practical implications for style classification and transfer tasks.

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.

Paper Structure

This paper contains 20 sections, 2 equations, 1 figure, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Explicitly Learning Style Representations
  4. Style Representations as a Byproduct
  5. Contrastive Learning for Text Representations
  6. Method
  7. Method
  8. Experiments
  9. Experimental Setup
  10. Fine-Tuning Data
  11. Evaluation Data
  12. Training
  13. Batch Construction
  14. Obtaining Generic Style Embeddings
  15. Results
  16. ...and 5 more sections

Figures (1)

  • Figure 1: Our training objective pushes sentence representations of the same style close together. In this example, reviews (in orange) are pushed close together, and sentences of one Bible version (in blue) are pushed close together, while the representations of different styles (Bible vs reviews) are pushed to be far apart.