Challenging Assumptions in Learning Generic Text Style Embeddings
This work addresses a gap in language representation learning for style-specific applications, though it appears incremental as it builds on existing methods without achieving the anticipated outcome.
The study tackled the problem of creating generic sentence-level style embeddings for style-centric tasks by fine-tuning a text encoder with contrastive learning and cross-entropy loss, but the results showed that the learned representations did not consistently capture high-level text styles.
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.