Same Author or Just Same Topic? Towards Content-Independent Style Representations (2204.04907v1)

Abstract: Linguistic style is an integral component of language. Recent advances in the development of style representations have increasingly used training objectives from authorship verification (AV): Do two texts have the same author? The assumption underlying the AV training task (same author approximates same writing style) enables self-supervised and, thus, extensive training. However, a good performance on the AV task does not ensure good "general-purpose" style representations. For example, as the same author might typically write about certain topics, representations trained on AV might also encode content information instead of style alone. We introduce a variation of the AV training task that controls for content using conversation or domain labels. We evaluate whether known style dimensions are represented and preferred over content information through an original variation to the recently proposed STEL framework. We find that representations trained by controlling for conversation are better than representations trained with domain or no content control at representing style independent from content.

• The paper introduces a novel framework that separates stylistic features from content using contrastive learning in authorship verification tasks.
• It employs modified verification setups with conversation-based content control on transformer models like RoBERTa and BERT.
• Evaluation shows that the CAV setup enhances style detection accuracy, offering practical benefits for forensic linguistics and profiling.

Towards Content-Independent Style Representations: Advancements in Authorship Verification

The paper "Same Author or Just Same Topic? Towards Content-Independent Style Representations" by Wegmann, Schraagen, and Nguyen presents an approach to disentangle linguistic style from content in computational models. This research addresses a significant issue in style representation learning, where authorship verification (AV) has been predominantly used, but often conflates style with content.

Overview and Objectives

The primary aim of this paper is to develop a framework for generating style representations that are less contaminated by subject matter-related content. This is achieved by controlling for content using proxies, such as conversation or domain labels, within the AV training paradigm. The authors introduce variations to the AV setup, notably the Contrastive Authorship Verification (CAV) setup, which incorporates a tertiary contrastive input to refine the learning process.

Methodological Insights

The authors implement a multi-faceted training regime using modified AV tasks, and fine-tune transformer-based models such as RoBERTa and BERT. A novel aspect of their training protocol is the incorporation of contrastive learning objectives alongside conversation-level content control. This allows the learning algorithm to focus more on stylistic features rather than on semantic or topical cues. By leveraging a large-scale corpus from Reddit, the authors curate diverse training instances to demonstrate the efficacy of their method across different content scenarios.

Evaluation and Findings

Evaluations across traditional AV and the novel CAV setups reveal that the models, when trained with conversation-based content control, exhibit enhanced performance in distinguishing style from content. Additionally, the paper introduces a modified evaluation known as STEL-Or-Content, particularly designed to test models' proficiency in differentiating stylistic traits from content-based variations. Notably, models trained on the CAV task with conversation-based control outperform others, indicating a stronger proclivity towards capturing content-independent style metrics.

Implications and Future Directions

This research opens several avenues for further investigation. The practical implications are immense in fields such as author profiling and forensic linguistics, where distinguishing an author’s stylistic signature from thematic content is crucial. Theoretically, the paper provides insights into the intertwining of style and content in language use, suggesting that comprehensive modeling must address this complexity.

In terms of future work, the authors propose extending their methodology to non-conversational datasets and exploring more granular content controls. Enhancements could include leveraging semantic embeddings to further purify style representations. Additionally, expanding on the evaluation framework to encompass a broader array of stylistic dimensions could fine-tune the system's ability to generalize across various language registers and dialects.

Conclusion

Wegmann et al.'s approach shines a light on the intricate dance between style and content in linguistic data, advancing the state-of-the-art in style representation learning. Their strategic modifications to the AV task foster a more refined understanding of style as an independent linguistic characteristic, marking a significant stride in computational stylometry. The provided code and datasets ensure the work's reproducibility, fostering continued exploration and innovation in this domain.