[Paper Review] Style is NOT a single variable: Case Studies for Cross-Style Language Understanding

Dongyeop Kang, Eduard Hovy|arXiv (Cornell University)|Nov 9, 2019

Natural Language Processing Techniques55 references4 citations

TL;DR

This paper introduces xSLUE, a benchmark corpus combining 15 styles across four theoretical groups—figurative, affective, personal, and interpersonal—for cross-style language understanding. It demonstrates that jointly training classifiers improves performance, reveals significant style inter-dependencies (e.g., impoliteness and offensiveness), and shows that contradictory style combinations (e.g., positive sentiment and impoliteness) lead to less stylistically appropriate text generation.

ABSTRACT

Every natural text is written in some style. Style is formed by a complex combination of different stylistic factors, including formality markers, emotions, metaphors, etc. One cannot form a complete understanding of a text without considering these factors. The factors combine and co-vary in complex ways to form styles. Studying the nature of the co-varying combinations sheds light on stylistic language in general, sometimes called cross-style language understanding. This paper provides the benchmark corpus (xSLUE) that combines existing datasets and collects a new one for sentence-level cross-style language understanding and evaluation. The benchmark contains text in 15 different styles under the proposed four theoretical groupings: figurative, personal, affective, and interpersonal groups. For valid evaluation, we collect an additional diagnostic set by annotating all 15 styles on the same text. Using xSLUE, we propose three interesting cross-style applications in classification, correlation, and generation. First, our proposed cross-style classifier trained with multiple styles together helps improve overall classification performance against individually-trained style classifiers. Second, our study shows that some styles are highly dependent on each other in human-written text. Finally, we find that combinations of some contradictive styles likely generate stylistically less appropriate text. We believe our benchmark and case studies help explore interesting future directions for cross-style research. The preprocessed datasets and code are publicly available.

Motivation & Objective

• To develop a comprehensive benchmark for cross-style language understanding that captures the co-variation of multiple stylistic factors in written text.
• To address the limitation of prior work that studies style dependencies in isolation or across narrow dimensions, by enabling multi-style analysis at scale.
• To evaluate the impact of joint style modeling on classification, correlation, and generation tasks using a unified, multi-style evaluation framework.
• To investigate whether style inter-dependencies in human-written text translate to model-generated text, and whether contradictory style combinations degrade generation quality.
• To provide publicly available datasets and code to support future research in cross-style NLP applications.

Proposed method

• Aggregated 15 distinct styles from existing datasets and introduced a new one, organizing them into four theoretical groups: figurative, affective, personal, and interpersonal.
• Constructed a diagnostic cross-style annotation set by labeling all 15 styles on the same set of sentences to enable valid evaluation of multi-style variation.
• Proposed a joint cross-style classifier trained on multiple styles simultaneously, improving performance over individually trained classifiers.
• Developed a style correlation analysis using human-annotated data to identify statistically significant inter-dependencies among styles in written text.
• Built a conditional stylistic text generator that conditions generation on multiple style labels using a product of style probabilities: $\mathrm{P}(x|s_1..s_k) \propto \mathrm{P}(x) \cdot \mathrm{P}(s_1|x)\cdots\mathrm{P}(s_k|x)$.
• Conducted human evaluation on generated text for stylistic appropriateness and consistency, using Likert-scale judgments and correlation scores from the style matrix.

Experimental results

Research questions

• RQ1 How does joint training on multiple styles improve classification performance compared to training on individual styles?
• RQ2 What are the statistically significant inter-dependencies between different styles in human-written text?
• RQ3 To what extent do contradictory style combinations (e.g., positive sentiment and impoliteness) degrade the stylistic appropriateness of generated text?
• RQ4 How does the performance of a style classifier correlate with the quality of style-controlled text generation?
• RQ5 Can observed correlations in human-written text be replicated in model-generated text, and do they reflect natural stylistic constraints?

Key findings

• The jointly trained cross-style classifier achieved better performance than individually trained classifiers, demonstrating the benefit of modeling style co-variation.
• Significant style inter-dependencies were found in human-written text, such as a strong correlation between impoliteness and offensiveness (p < 0.01).
• Human evaluation showed that combinations of contradictory styles—like positive sentiment and impoliteness—resulted in lower stylistic appropriateness (mean score 2.45 vs. 3.11 for non-contradictory pairs).
• Stylistic appropriateness scores in generated text closely mirrored the correlation scores observed in human-written text, indicating that models learn natural style constraints.
• As classifier training progress increased (measured by completion ratio), both stylistic appropriateness and consistency in generated text improved, confirming a strong link between classification quality and generation quality.
• Text generated with negative labels (e.g., impoliteness, offensiveness) showed lower stylistic appropriateness than positive or literal labels, suggesting inherent challenges in generating certain style combinations.