Finally, Section 2.3 lists all the common subtasks for neural TST which can save the literature review efforts for future researchers. In this paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the first neural text style transfer work in 2017. 2018; Li et al. Some TST works have been inspired by MT, such as the pseudo-parallel construction (Nikolov and Hahnloser 2019; Zhang et al. The rst one is by linguistic denition . (2017) evaluated how the generated text as augmented data can improve the downstream attribute classification accuracy. Image style transfer has been an important branch of computer vision and image processing. Style transfer is a task to recompose the content of an image in the style of another. Besides this difference, many other aspects of style transfer research can have shared nature. 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 . We have surveyed recent research efforts in TST and developed schemes to categorize and distill the existing literature. 2020). A sentences own perplexity will change if the sentence prior to it changes. The second approach, Attribute Code Control (ACC), as shown in Figure 2b, first enforces the latent representation z of the sentence x to contain all information except its attribute value a via adversarial learning, and then the transferred output is decoded based on the combination of z and a structured attribute code a corresponding to the attribute value a. PDF Deep Learning for Text Style Transfer: A Survey Text style transfer is an important task in natural language generation, which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. As a remedy, we encourage future researchers to report inter-rater agreement scores such as the Cohens kappa (Cohen 1960) and Krippendorffs alpha (Krippendorff 2018). ( Image credit: A Neural Algorithm of Artistic Style ) Benchmarks Add a Result These leaderboards are used to track progress in Style Transfer Libraries Use these libraries to find Style Transfer models and implementations 2020). Disentanglement-based models usually perform the following three actions: Encode the text x with attribute a into a latent representation z (i.e., x z), Manipulate the latent representation z to remove the source attribute (i.e., z z), Decode into text x with the target attribute a (i.e., z x). We will introduce in this section several closely related tasks, including machine translation, image style transfer, style-conditioned language modeling, counterfactual story rewriting, contrastive text generation, and prototype-based text editing. Using a similar approach, Madaan et al. The gender-related TST dataset is proposed by Prabhumoye et al. GitHub - oneTaken/style_transfer_survey: A survey on style_transfer This framework is widely applied to NLG tasks (e.g., Zue and Glass 2000; Mani 2001; McTear 2002; Gatt and Reiter 2009; Androutsopoulos and Malakasiotis 2010). If parallel data are provided, standard sequence-to-sequence models are often directly applied (Rao and Tetreault 2018) (see Section 4). Currently, it is a simplification of the problem setting to limit it to scenarios where the attribute and semantics can be approximately separated. Knowing the new advances in prototype editing for other tasks can potentially inspire new method innovations in TST. As shown in Jin et al. 2016), genre, and attributes derived from text, topics, and sentiment (Ficler and Goldberg 2017). Abstract. Computational Linguistics 2022; 48 (1): 155205. Specifically, DAE first passes the input sentence x through a noise model to randomly drop, shuffle, or mask some words, and then reconstructs the original sentence from this corrupted sentence. Linguistic phenomena related to gender is a heated research area (Trudgill 1972; Lakoff 1973; Tannen 1990; Argamon et al. However, we have found by ablative experiments that optimizing an image in the way neural style transfer does, while the objective functions (or more precisely, thefunctionstotransformrawimagestocorresponding feature maps being compared) are constructed without pretrained weights or biases, worked almost as well. (2018) first propose to borrow the FlickrStyle stylized caption dataset (Gan et al. Content-oriented losses are more often used for this aim (Section 5.1.3.2). Another use of style transferred text is adversarial robustness probing. This is the reading list for text style transfer papers maintained by Zhijing Jin at Max Planck Institute for Intelligent Systems, Tbingen. Categorical reparameterization with Gumbel-Softmax, 5th International Conference on Learning Representations, ICLR 2017, Shakespearizing modern language using copy-enriched sequence-to-sequence models, Adversarial examples for evaluating reading comprehension systems, Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, EMNLP 2017, Hooks in the headline: Learning to generate headlines with controlled styles, Is BERT really robust? 2019) can lead to improvement by at least 7 BLEU points with human references Wang et al. They are currently limited to a small set of pre-defined condition tokens and can only generate from scratch a sentence, but are not yet able to be conditioned on an original sentence for style rewriting. Note that this style classifier usually reports 80+% or 90+% accuracy, and we will discuss the problem of false positives and false negatives in the last paragraph of this section. Extracting attribute markers is a non-trivial NLP task. The data-driven definition of style is different from the linguistic or rule-based definition of style, which theoretically constrains what constitutes a style and what not, such as a style guide (e.g., American Psychological Association 2020) that requires that formal text not include any contraction, e.g., isnt. The distinction of the two defintions of style is shown in Figure 1. Init convolution layer has a big kernel size to have a bigger receptive field. (2019) and Jin et al. After deleting the attribute markers Markera(x) of the sentence x with attribute a, we need to find a counterpart attribute marker Markera(x) from another sentence x carrying a different attribute a. Just as everyone has their own signatures, style originates as the characteristics inherent to every persons utterance, which can be expressed through the use of certain stylistic devices such as metaphors, as well as choice of words, syntactic structures, and so on. Note that there can be future works that do not take such an assumption, which will be discussed in Section 6.3. The third approach, Latent Representation Splitting (LRS), as illustrated in Figure 2c, first disentangles the input text into two parts: the latent attribute representation a, and semantic representation z that captures attribute-independent information. For the manipulation method chosen above, select (multiple) appropriate loss functions (Section 5.1.3). 2020; Guo et al. Theoretically, although disentanglement is impossible without inductive biases or other forms of supervision (Locatello et al. Table 5 provides an overview of existing models and their corresponding configurations. Di Jin, Zhijing Jin, Zhiting Hu, Olga Vechtomova, Rada Mihalcea; Deep Learning for Text Style Transfer: A Survey. For example, Gardner et al. Answers under two topics, entertainment and politics, respectively. As neural network-based NLP models more easily learn spurious statistical correlations in the data rather than achieve robust understanding (Jia and Liang 2017), there is recent work to construct auxillary datasets composed of near-misses of the original data. It regards style as the attributes that vary across datasets, as opposed to the characteristics that stay invariant (Mou and Vechtomova 2020). An illustrative example is that if the style classifier only reports 80+% performance (e.g., on the gender dataset [Prabhumoye et al. Such extension of styles is driven by the advancement of TST methods, and also various downstream needs, such as persona-based dialog generation, customized text rewriting applications, and moderation of online text. As most content words are kept and no additional information is hallucinated by the black-box neural networks, we can better ensure that the information of the attribute-transferred output is consistent with the original input. Overview of evaluation methods for each criterion. (2018) detect the attribute markers by calculating its relative frequency of co-occurrence with attribute a versus a, and those with frequencies higher than a threshold are considered the markers of a. 2019), does not learn the word translation table, and instead trains the initial style transfer models on a retrieval-based pseudo-parallel corpora introduced in the retrieval-based corpora construction above. For example, the source sentence can be a positive review on an Asian restaurant written by a male reviewer, and the target sentence is a negative review on an American restaurant written by a female. Their third dataset, Social Media Content dataset, collected from internal Facebook data that is private, contains gender (male or female), age group (1824 or 65+), and writer-annotated feeling (relaxed or annoyed). Personalizing dialogue agents: I have a dog, do you have pets too? Apart from the existing scoring methods, future work can also make use of linguistic rules such as a checklist to evaluate what capabilities the TST model has achieved. There are three model choices to obtain the latent representation z from the discrete text x and then decode it into the new text x via reconstruction training: auto-encoder (AE), variational auto-encoder (VAE), and generative adversarial networks (GANs). In terms of the criteria, humans can provide overall evaluation, or separate scores for transferred style strength, semantic preservation, and fluency. 2012; Jhamtani et al. Using style transfer, users can share and exchange images; painters can create specific art styles more readily with less creation cost and production . When disentangling the attribute information a and the attribute-independent semantic information z, we need to achieve two aims: The target attribute is fully and exclusively controlled by a (and not z). (2019) propose the first dataset, each sample of which takes an originally five-sentence story, and changes the event in the second sentence to a new, counterfactual event. For example, Reiter, Robertson, and Osman (2003) evaluated the effect of their tailored text on reducing smokers intent to smoke through clinical trials. In this section, we will cover TST methods on parallel datasets, and in Section 5 we will detail the approaches on non-parallel datasets. Because these generation practices are complicated, Madaan et al. Neural Style Transfer. 2019). 2020) also include text generation conditioned on a style token, such as positive or negative. Note that there are style transfer works across different modalities, including images (Gatys, Ecker, and Bethge 2016; Zhu et al. Tran, Zhang, and Soleymani (2020) collect 350K offensive sentences and 7M non-offensive sentences by crawling sentences from Reddit using a list of restricted words. In the FlickrStyle image caption dataset, each image has three captions, with a factual, a humorous, and a romantic style, respectively. Style versus content: A distinction without a (learnable) difference? TST can be applied not only to other NLP tasks as introduced in the previous section, but also can be helpful for specialized downstream applications. 2018; Dai et al. We also survey 23 style transfer papers and discover that existing automatic metrics can be easily gamed and propose fixed variants. Published under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) license. We define the main notations used in this survey in Table 2. 2018; Gong et al. TST is a good method for data augmentation because TST can produce text with different styles but the same meaning. The style corpora can be parallel or non-parallel. For example, Wu et al. In this section, we will propose some potential directions for future TST research, including expanding the scope of styles (Section 6.1), improving the methodology (Section 6.2), loosening the style-specific data assumptions (Section 6.3), and improving evaluation metrics (Section 6.4). The third point is a direct result implied by the second point, so in practice, we need to keep in mind and check whether the proposed model takes advantage of the evaluation metrics or makes improvements that are generalizable. The goal of text style transfer (TST) is to automatically control the style attributes of text while preserving the content. Math data: https://gitlab.cs.washington.edu/kedzior/Rewriter/. Apart from the additional loss designs, using the pretrained language model GPT-2 (Radford et al. (2018), accuracy (Acc. 2002), ROUGE (Lin and Och 2004), METEOR (Banerjee and Lavie 2005), chrF (Popovi 2015), and Word Mover Distance (WMD) (Kusner et al. Text_Style_Transfer_Survey | #Natural Language Processing | This repo Such data privacy widely exists in the data science community as a whole, and there have been many ethics discussions (Tse et al. Recent advances of conditional language models (Keskar et al. Infill the sentence by adding new attribute markers and make sure the generated sentence is fluent (Section 5.2.3). As an extension to the retrieve and edit steps, Hossain, Ghazvininejad, and Zettlemoyer (2020) use an ensemble approach to retrieve a set of relevant prototypes, edit, and finally rerank to pick the best output for machine translation. In machine translation, it is useful to have an additional control of the style for the translated text. NLP research and applications, including TST, that directly involve human users, is regulated under a central regulatory board, the Institutional Review Board (IRB). Many concerns have been raised about the discriminative task of author profiling, which can mine the demographic identities of the author of a writing, even including privacy-invading properties such as gender and age (Schler et al. It has a long history in the field of natural language processing (NLP), and recently has re-gained significant attention thanks to the promising performance brought by deep neural models. of the three mainstreams of TST methods on non-parallel data. In this . There is also a recent dataset with 21 text styles such as Sciences, Sport, Politics, and others (Zeng, Shoeybi, and Liu 2020). The increasing interest in modeling the style of text can be regarded as a trend reflecting the fact that NLP researchers start to focus more on user-centeredness and personalization. Contribute to sroylee/Text_Style_Transfer_Survey development by creating an account on GitHub. With the success of deep learning in the last decade, a variety of neural methods have been recently proposed for TST. (2021) have proposed a new framework that is a live environment to evaluate NLG in a principled and reproducible manner. 2018). A successful style-transferred output not only needs to demonstrate the correct target style, but also, due to the uncontrollability of neural networks, we need to verify that it preserves the. For the (non-data-driven) linguistic style, although it is under-explored in the existing deep learning works of TST, we provide in Section 6.3 a discussion of how potential future works can learn TST of linguistics styles with no matched data. Jin et al. Another paradigm soon followed, namely, pseudo-parallel corpus construction to train the model as if in a supervised way with the pseudo-parallel data (Zhang et al. Another way, adopted by Zhang et al. The attribute-independent information is fully and exclusively captured by z (and not a). 2019). Forty (40) lucky participants will win a $50 gift card! This line of research is relatively difficult to directly apply to TST, because its motivation and dataset nature is different from the general TST, and more importantly, this task is not conditioned on a predefined categorized style token, but the free-form textual story beginning. Another task, political slant transfer, may induce concerns within some specific context. 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