Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020)

Abstract: Modern NLP defines the task of style transfer as modifying the style of a given sentence without appreciably changing its semantics, which implies that the outputs of style transfer systems should be paraphrases of their inputs. However, many existing systems purportedly designed for style transfer inherently warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. In this paper, we reformulate unsupervised style transfer as a paraphrase generation problem, and present a simple methodology based on fine-tuning pretrained language models on automatically generated paraphrase data. Despite its simplicity, our method significantly outperforms state-of-the-art style transfer systems on both human and automatic evaluations. We also survey 23 style transfer papers and discover that existing automatic metrics can be easily gamed and propose fixed variants. Finally, we pivot to a more real-world style transfer setting by collecting a large dataset of 15M sentences in 11 diverse styles, which we use for an in-depth analysis of our system.

## introduction the task of style transfer on text data involves changing the style of a given sentence while preserving its semantics. 1 recent work in this area conflates style transfer with the related task of attribute transfer @xcite , in which modifications to attributespecific content words (e.g., those that carry sentiment) warp both stylistic and semantic properties of a sentence @xcite . attribute transfer has been criticized for its limited real-world applications: pang (2019) argue that se- why, uncle, 'tis a shame no lie… i would jump in why, uncle, 'tis a shame no lie… i would jump in o, wilt thou leave me so unsatisfied? it's a shame, uncle i'd jump in there, no doubt oh, you're gonna leave me unsatisfied, right? ooh yall will leave me unhappy lol step 1: diverse paraphrasing step 2: inverse paraphrasing (shakespeare, twitter) ## style transfer via paraphrasing we loosely define style as common patterns of lexical choice and syntactic constructions that are distinct from the content of a sentence, following prior work @xcite @xcite . while we acknowledge this distinction is not universally accepted, foot_2 this treatment is critical to unlock several real-world applications of style transfer (as argued in section 1). unfortunately, many modern style transfer systems do not respect this definition: a human evaluation ( ## evaluating style transfer providing a meaningful comparison of our approach to existing style transfer systems is difficult because of (1) poorly-defined automatic and human methods for measuring style transfer quality @xcite @xcite , and (2) misleading (or absent) methods of aggregating three individual metrics (transfer accuracy, semantic similarity and fluency) into a single number. in this section, we describe the flaws in existing metrics and their aggregation (the latter illustrated through a naïve baseline), and we propose a new evaluation methodology to fix these issues. ## experiments & results we evaluate our method (strap) on two existing style transfer datasets, using the evaluation methodology proposed in section 3. our system significantly outperforms state of the art methods and the naïve baseline discussed in section 3.2. ## ablation studies in this section, we perform several ablations on strap to understand which of its components contribute most to its improvements over baselines. overall, these ablations validate the importance of both paraphrasing and pretraining for style transfer. paraphrase diversity improves acc: how critical is diversity in the paraphrase generation step? while our implementation of f para is trained on data that is heavily-filtered to promote diversity, we also build a non-diverse paraphrase model by removing this diversity filtering of paranmt-50m but keeping all other experimental settings identical. in ## towards real-world style transfer all of our experiments and ablations thus far have been on the shakespeare and formality datasets, which contain just two styles each. to explore the ability of our system to perform style transfer between many diverse styles, we create the corpus of diverse styles (cds), a new non-parallel style transfer benchmark dataset with 11 diverse styles (15m tokens), and use it to evaluate strap. ## related work unsupervised style transfer is often modeled by disentangling style & content using attribute classifiers @xcite , policy gradient training @xcite or retrieval-based approaches @xcite . recently, backtranslation has emerged as a method to model semantic preservation (prabhumoye et al., 19 we could not find an easy way to perform 11-way style transfer in the baseline models without significantly modifying their codebase / model due to the complex probabilistic formulation beyond 2 styles and separate modeling for each of the 110 directions. 2018 ), but this method can also warp semantics as seen in @xcite ; as such, we only use it to build our paraphraser's training data after heavy filtering. our work relates to recent efforts that use transformers in style transfer @xcite . closely related to our work is @xcite , who over-generate paraphrases using a complex handcrafted pipeline and filter them using proximity to a target style corpus. instead, we automatically learn style-specific paraphrasers and do not need over-generation at inference. relatedly, preotiuc-pietro et al. ( 2016 ) present qualitative style transfer results with statistical mt paraphrasers. other, less closely related work on control & diversity in text generation is discussed in appendix a.12. ## conclusion in this work we model style transfer as a controlled paraphrase generation task and present a simple unsupervised style transfer method using diverse paraphrasing. we critique current style transfer evaluation using a survey of 23 papers and propose fixes to common shortcomings. finally, we collect a new dataset containing 15m sentences from 11 diverse styles. possible future work includes (1) exploring other applications of diverse paraphrasing, such as data augmentation; (2) performing style transfer at a paragraph level; (3) performing style transfer for styles unseen during training, using few exemplars provided during inference. umass nlp group for help at various stages of the project. finally, we thank arka sadhu, richard pang, kevin gimpel, graham neubig, diyi yang, shrimai prabhumoye, junxian he and yonatan belinkov for insightful discussions. this research was supported in part by a research gift from adobe.