Challenging Assumptions in Learning Generic Text Style Embeddings

2025-01-27

AI Summary

Current language models struggle to model the hierarchical nature of textual style, particularly exhibiting a fundamental limitation in synthesizing high-level stylistic attributes—such as formality or irony—from low-level features like lexical choice and punctuation. This work is the first to systematically challenge the implicit assumption that low-level style transfer suffices for high-level style representation. We argue that style embeddings must be semantically disentangled and propose a sentence-level style discrimination framework built upon BERT/RoBERTa, trained via contrastive learning. Experiments show that the learned embeddings effectively distinguish basic affective dimensions (e.g., valence, arousal) but exhibit significant generalization failure on higher-order styles—including formality and irony. Our findings expose structural deficiencies in current stylistic representation learning, both in underlying modeling assumptions and evaluation paradigms, offering both theoretical insights and an empirical benchmark for future research.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Knowledge Representation and Reasoning: Argumentation
• Machine Learning: Representation Learning

Application Category

• Graph Algorithms and Modeling for the Web: Graph embeddings and representation learning for Web-related graphs
• Web Mining and Content Analysis: Large pretrained models with web data
• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics

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.

Problem

• Language Learning Models
• Text Style Diversity
• Complex Style Variation

Innovation

• Universal Sentence Style Model
• Complex Style Combination Recognition
• Diversified Stylistic Learning