Numerous recent techniques for text style transfer characterize their approaches as variants of reinforcement learning and preference optimization. In this work, we consider the relationship between these approaches and a class of optimization approaches developed primarily for (non-neural) statistical machine translation, formerly known as ‘tuning’. Inspired by these techniques from the past, we improve upon established preference optimization approaches, incorporating multiple iterations of exploration and optimization, and choosing contrastive examples by following a ‘hope’ vs ‘fear’ sampling strategy. Cognizant of the difference between machine translation and style transfer, however, we further tailor our framework with a new pseudo-parallel data generation method and a dynamic weighted reward aggregation method to tackle the lack of parallel data and the need for a multi-objective reward. We evaluate our model on two commonly used text style transfer datasets. Through automatic and human evaluation results we show the effectiveness and the superiority of our model compared to state-of-the-art baselines.

Authorship verification (AV) is a crucial task for applications like identity verification, plagiarism detection, and AI-generated text identification. However, datasets for training and evaluating AV models are primarily in English and primarily in a single domain. This precludes analysis of AV techniques for generalizability and can cause seemingly valid AV solutions to, in fact, rely on topic-based features rather than actual authorship features. To address this limitation, we introduce the Million Authors Corpus (), a novel dataset encompassing contributions from dozens of languages on Wikipedia. It includes only long and contiguous textual chunks taken from Wikipedia edits and links those texts to their authors. includes 60.08M textual chunks, contributed by 1.29M Wikipedia authors. It enables broad-scale cross-lingual and cross-domain AV evaluation to ensure accurate analysis of model capabilities that are not overly optimistic. We provide baseline evaluations using state-of-the-art AV models as well as information retrieval models that are not AV-specific in order to demonstrate ‘s unique cross-lingual and cross-domain ablation capabilities.