style transfer survey

We discuss the 2017). The Yelp Gender dataset is from the Yelp Challenge https://www.yelp.com/dataset and its preprocessing needs to follow Prabhumoye et al. 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 the evaluation metrics that rely on the pretrained models, namely, the style classifier and LM, we need to beware of the following: The pretrained models for automatic evaluation should be separate from the proposed TST model. 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? This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). There are several advantages in merging the traditional NLG with the deep learning models. A sentences own perplexity will change if the sentence prior to it changes. The style of language is crucial because it makes natural language processing more user-centered. 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. 2017) and positive-vs.-negative Yelp reviews (Shen et al. (2020). Cookies help us deliver our services. A comprehensive overview of the field is introduced in our survey Deep Learning for Text Style Transfer: A Survey (2020) by Di Jin*, Zhijing Jin* (Equal contribution), Zhiting Hu, Olga Vechtomova, and Rada Mihalcea. One common way to construct pseudo-parallel data is through retrieval, namely, extracting aligned sentence pairs from two mono-style corpora. NLP research and applications, including TST, that directly involve human users, is regulated under a central regulatory board, the Institutional Review Board (IRB). There are also evaluation metrics that are specific for TST such as the Part-of-Speech distance (Tian, Hu, and Yu 2018). In contrast, machine translation does not have this concern, because the vocabulary of its input and output are different, and copying the input sequence does not give high BLEU scores. Personalizing dialogue agents: I have a dog, do you have pets too? There are still remaining limitations of the previous methods, such as imperfect accuracy of the attribute classifier, and unclear relation between attribute and attention scores. of the three mainstreams of TST methods on non-parallel data. (2019) propose a more challenging setting of text attribute transfer: multi-attribute transfer. To achieve Aim 1, many different style-oriented losses have been proposed, to nudge the model to learn a more clearly disentangled a and exclude the attribute information from z. (2017) evaluated how the generated text as augmented data can improve the downstream attribute classification accuracy. Thirdly, the evaluation metrics of the two tasks can also inspire each other. Another way is through generation, such as iterative back-translation (IBT) (Hoang et al. Select a manipulation method of the latent representation (Section 5.1.2). Jin et al. Xu et al. However, due to the complexities of natural language, each metric introduced below can address certain aspects, but also has intrinsic blind spots. 2016; Merity et al. 2015; Russell, Dewey, and Tegmark 2015). ( 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 Hence, we suggest the research community raise serious concern against the review sentiment modification task. Style transfer of text is controlled paraphrase generation. 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. By taking a content image and a style image, the neural network can recombine the content and the style image to effectively creating an artistic ( recomposed) image. In this article, 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. Guu et al. (2018) first proposes the protype editing approach to improve LM by first sampling a lexically similar sentence prototype and then editing it using variational encoder and decoders. Finally, we will suggest some standard practice of TST evaluation for future work. However, most use cases do not have parallel data, so TST on non-parallel corpora has become a prolific research area (see Section 5). Application Programming Interfaces 120. For the manipulation method chosen above, select (multiple) appropriate loss functions (Section 5.1.3). There are three problems with using BLEU between the gold references and model outputs: It mainly evaluates content and simply copying the input can result in high BLEU scores. An intuitive notion of style refers to the manner in which the semantics is expressed (McDonald and Pustejovsky 1985). This approach is extended by Nikolov and Hahnloser (2019), who use large-scale hierarchical alignment to extract pseudo-parallel style transfer pairs. 2020; Ma et al. However, despite the growing interest in TST, the existing literature shows a large diversity in the selection of benchmark datasets, methodological frameworks, and evaluation metrics. These three directions, (1) disentanglement, (2) prototype editing, and (3) pseudo-parallel corpus construction, are further advanced with the emergence of Transformer-based models (Sudhakar, Upadhyay, and Maheswaran 2019; Malmi, Severyn, and Rothe 2020). As a potential solution, TST can be applied to alter the text and obfuscate the real identity of the users (Reddy and Knight 2016; Grndahl and Asokan 2020). Paraphrase generation is to express the same information in alternative ways (Madnani and Dorr 2010). One way to enhance the convergence of IBT is to add additional losses. First, TST can be used to help other NLP tasks such as paraphrasing, data augmentation, and adversarial robustness probing (Section 7.1). 2020b), while the styles that can change the task output can be used to construct contrast sets (e.g., sentiment transfer to probe sentiment classification robustness) (Xing et al. Neural Style Transfer. By continuing to use our website, you are agreeing to, Acid Rain Science and Politics in Japan: A History of Knowledge and Action toward Sustainability, Survey of Architectural History in Cambridge: Northwest Cambridge, Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, https://creativecommons.org/licenses/by-nc-nd/4.0/legalcode, https://github.com/fuzhenxin/Style-Transfer-in-Text, https://github.com/zhijing-jin/Text_Style_Transfer_Survey, https://github.com/raosudha89/GYAFC-corpus, https://github.com/tag-and-generate/politeness-dataset, https://github.com/lijuncen/Sentiment-and-Style-Transfer/tree/master/data/imagecaption, https://github.com/keithecarlson/StyleTransferBibleData, https://mimic.physionet.org/gettingstarted/access/, https://srhthu.github.io/expertise-style-transfer/, https://gitlab.cs.washington.edu/kedzior/Rewriter/, https://github.com/shentianxiao/language-style-transfer, https://github.com/lijuncen/Sentiment-and-Style-Transfer/tree/master/data/amazon, https://webscope.sandbox.yahoo.com/catalog.php?datatype=l&did=11, https://nlp.stanford.edu/robvoigt/rtgender/, https://doi.org/10.1017/S1351324907004664, https://aclanthology.org/2021.naacl-main.256, https://www.aclweb.org/anthology/2020.acl-main.100, https://doi.org/10.18653/v1/2020.emnlp-main.524, https://doi.org/10.1177/001316446002000104, https://doi.org/10.18653/v1/2020.emnlp-main.475, https://doi.org/10.1109/ICASSP.2018.8462018, https://doi.org/10.18653/v1/2020.findings-emnlp.117, https://doi.org/10.18653/v1/2021.gem-1.10, https://www.wired.com/story/twitter-instagram-unveil-new-ways-combat-hate-again/, https://doi.org/10.18653/v1/2020.acl-main.561, https://doi.org/10.18653/v1/2020.acl-main.228, https://doi.org/10.1016/0378-2166(87)90099-3, https://doi.org/10.1016/0004-3702(90)90084-D, https://doi.org/10.18653/v1/2020.coling-main.201, https://doi.org/10.18653/v1/2020.coling-main.197, https://doi.org/10.18653/v1/2020.acl-main.456, https://doi.org/10.1007/978-3-319-65813-1_18, https://doi.org/10.18653/v1/2020.emnlp-main.55, https://doi.org/10.18653/v1/2021.acl-short.62, https://doi.org/10.18653/v1/2021.acl-long.353, https://doi.org/10.18653/v1/2020.emnlp-main.602, https://doi.org/10.18653/v1/2020.acl-main.169, https://doi.org/10.18653/v1/2020.emnlp-main.699, https://doi.org/10.18653/v1/2020.acl-tutorials.5, https://doi.org/10.1162/089120105775299168, https://doi.org/10.26615/978-954-452-056-4_098, https://doi.org/10.1108/S1537-466120140000018021, https://doi.org/10.18653/v1/2020.emnlp-main.89, https://doi.org/10.18653/v1/2021.naacl-main.410, https://doi.org/10.1017/S1351324997001502, https://doi.org/10.1016/S0004-3702(02)00370-3, https://doi.org/10.1016/j.artint.2005.06.006, https://doi.org/10.18653/v1/2020.acl-main.320, https://doi.org/10.18653/v1/2021.nlp4convai-1.20, https://doi.org/10.18653/v1/2021.acl-long.293, https://doi.org/10.18653/v1/2021.eacl-main.24, https://doi.org/10.1007/978-3-319-45886-1_3, https://doi.org/10.1007/978-3-030-45439-5_36, https://doi.org/10.18653/v1/2021.naacl-main.208, https://doi.org/10.1038/s41467-018-06930-7, https://doi.org/10.18653/v1/2020.acl-main.219, https://doi.org/10.1080/01638539009544747, https://doi.org/10.1017/S0047404500000488, https://doi.org/10.1007/s10676-015-9369-6, https://doi.org/10.18653/v1/2020.emnlp-main.292, https://doi.org/10.18653/v1/2020.acl-main.294, A Survey on Deep Learning for Multimodal Data Fusion, Cross-Entropy Pruning for Compressing Convolutional Neural Networks, Safer Reinforcement Learning through Transferable Instinct Networks, Explanation-Based Human Debugging of NLP Models: A Survey, Martha Palmer and Barbara Di Eugenio Interview Martha Evens, An attribute value, e.g., the formal style, A corpus of sentences with attribute value, Latent representation of the attribute value in text, Reddit Politics (Tran, Zhang, and Soleymani, Accuracy by a separately trained style classifier, Perplexity by a separately trained language model, + More profound in theoretical analysis, e.g., disentangled representation learning, Difficulties of training deep generative models (VAEs, GANs) for text, Hard to represent all styles as latent code, Computational cost rises with the number of styles to model, + High BLEU scores due to large word preservation, Attribute marker detection step can fail if the style and semantics are confounded, The step target attribute retrieval by templates can fail if there are large rewrites for styles, e.g., Shakespearean English vs. modern English, Target attribute retrieval step has large complexity (quadratic to the number of sentences), Large computational cost if there are many styles, each of which needs a pre-trained LM for the generation step, ? University of Waterloo, Faculty of Engineering. 2020), medical text simplification (Cao 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. Hence, Lee (2020) propose word importance scoring, similar to what is used by Jin et al. Counterfactual story rewriting aims to learn a new event sequence in the presence of a perturbation of a previous event (i.e., counterfactual condition) (Goodman 1947; Starr 2019). BLEU is shown to have low correlation with human evaluation. Traditional approaches rely on term replacement and templates. It can be replaced by any other 2020) aims to control the politeness in text. Among Steps 3 to 6, sentence aggregation groups necessary information into a single sentence, lexicalization chooses the right word to express the concepts generated by sentence aggregation, referring expression generation produces surface linguistic forms for domain entities, and linguistic realization edits the text so that it conforms to grammar, including syntax, morphology, and orthography. Inspired by the development of deep learning, applications of Convolutional Neural Networks (CNNs) in recomposing content and style of two separated images were proven to be effective by the recent works of image style transfer. For example, Wu et al. Similar to many text generation tasks, TST also has human-written references on several datasets (Yelp, Captions, etc. Using the language of the traditional NLG framework, the prototype-based techniques can be viewed as a combination of sentence aggregation, lexicalization, and linguistic realization. MSD data: https://srhthu.github.io/expertise-style-transfer/. 2017; Hu et al. (2021) use cycle training with a conditional variational auto-encoder to unsupervisedly learn to express the same semantics through different styles. Some TST works have been inspired by MT, such as the pseudo-parallel construction (Nikolov and Hahnloser 2019; Zhang et al. We discuss the task formulation, existing datasets and subtasks, evaluation, as well as the rich methodologies in the presence of parallel and non-parallel data. 2017) and Amazon reviews (He and McAuley 2016). (2020b) for adversarial paraphrasing, to measure how important a token is to the attribute by the difference in the attribute probability of the original sentence and that after deleting a token. In these tasks, each data point is one sentence with a clear, categorized style, and the entire dataset is in the same domain. Specifically, image style transfer technique is to specify an input image as the base image, also known as the content image. Yahoo! Here, I'll walk through a machine learning project I recently did in a tutorial-like manner. 2018). Retrieve candidate attribute markers carrying the desired attribute a (Section 5.2.2). Madaan et al. For instance, one such application is intelligent bots, for which users prefer distinct and consistent persona (e.g., empathetic) instead of emotionless or inconsistent persona. A Survey of Research on Image Style Transfer Based on Deep Learning Abstract: The explosive growth of graphics card computing power is accompanied by the rise of deep learning, and the development of style transfer has also ushered in a new stage. 2019). Style Transfer makes it simple to transform image sets and is perfect for creating matching series of artworks and collectibles. Hence, various methods have been proposed for data augmentation to enrich the data. Because these generation practices are complicated, Madaan et al. First, pytorch has a official example fast_neural_style. (2019) set a threshold to filter out low-quality attribute markers by frequency-ratio methods, and in cases where all attribute markers are deleted, they use the markers predicted by attention-based methods. First, many trained TST models can be borrowed for paraphrasing, such as formality transfer and simplification. 2018). Ser. 2017) and Amazon product reviews (He and McAuley 2016). ), BLEU with the input (BL-Inp), and perplexity (PPL). In general, they can be categorized based on whether the dataset has parallel text with different styles or several non-parallel mono-style corpora. To learn the attribute-independent information fully and exclusively in z, the following content-oriented losses are proposed: One way to train the above cycle loss is by reinforcement learning as done by Luo et al. It regards style as the attributes that vary across datasets, as opposed to the characteristics that stay invariant (Mou and Vechtomova 2020). (2021) showed that METEOR and WMD have better correlation with human evaluation than BLEU, although, in practice, BLEU is the most widely used metric to evaluate the semantic similarity between the source sentence and style-transferred output (Yang et al. For example, Wu, Wang, and Liu (2020) translate from informal Chinese to formal English. IBT is a widely used method in machine translation (Artetxe et al. Copy mechanism (Glehre et al. (2021a) also recommends standardizing and describing evaluation protocols (e.g., linguistic background of the annotators, compensation, detailed annotation instructions for each evaluation aspect), and releasing annotations. TST is a good method for data augmentation because TST can produce text with different styles but the same meaning. As covered by this survey, the early work on deep learning-based TST explores relatively simple styles, such as verb tenses (Hu et al. As an unsupervised training model, GAN has been widely used in the field of computer vision, especially . 2020) is the first corpus of biased and neutralized sentence pairs. In practice, some big challenges for disentanglement-based methods include, for example, the difficulty to train deep text generative models such as VAEs and GANs. Many new advances in one style transfer field can inspire another style transfer field. 2019) uses a checking mechanism instead of additional losses. 2017; Fu et al. Most recently, Zhang, Ge, and Sun (2020) use a data augmentation technique by making use of largely available online text. Interesting future directions can be reducing the computational cost, designing more effective bootstrapping, and improving the convergence of IBT. Computational Linguistics 2022; 48 (1): 155205. Because conversational agents directly interact with users, there is a strong demand for human-like dialog generation. 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. The collection and potential use of such sensitive user attributes can have implications that need to be carefully considered. Inspired by the development of deep learning, applications of Convolutional Neural Networks . (2020) propose a simpler way. Note that we interchangeably use the terms style and attribute in this survey. 2018; Madaan et al. (2021) follow this direction and propose a disentanglement-based model to generate attractive headlines for Chinese news. (2020b) showed that merely paraphrasing using synonyms can drop the performance of high-accuracy classification models from TextCNN (Kim 2014) to BERT (Devlin et al. Besides syntactic variation, it also makes sense to include stylistic variation as a form of paraphrases, which means that the linguistic style transfer (not the content preference transfer in Table 3) can be regarded as a subset of paraphrasing. Xu et al. It is collected from Wikipedia revisions that adjusted the tone of existing sentences to a more neutral voice. 2018), and question answering (Lewis et al. They skip Step 2 that explicitly retrieves attribute candidates, and, instead, directly learn a generation model that only takes attribute-masked sentences as inputs. The four middle-level representations can also be chosen as other. Performing human evaluations can be time consuming, which may result in significant time and financial costs. We analyze the three major streams of approaches for unsupervised TST in Table 6, including their strengths, weaknesses, and future directions. Based on such initial corpora, they train initial style transfer models and bootstrap the IBT process. 2019), disentanglement is achievable with some weak signals, such as only knowing how many factors have changed, but not which ones (Locatello et al. Also, it is not easy to represent all styles as latent code. 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. Lastly, this work is limited in the scope of evaluations. Search for other works by this author on: Max Planck Institute for Intelligent Systems, Empirical Inference Department and ETH Zrich, Department of Computer Science. Style can also go beyond the sentence level to the discourse level, such as the stylistic structure of the entire piece of the work, for example, stream of consciousness, or flashbacks. Math data: https://gitlab.cs.washington.edu/kedzior/Rewriter/. Two major approaches are retrieval-based and generation-based methods. 2020), speaker identity, persona and emotion (Li et al. https://iopscience.iop.org/article/10.1088/1742-6596/1453/1/012129 from Text style transfer (TST) is an important task in natural language generation (NLG), which aims to control certain attributes in the generated text, such as politeness, emotion, humor, and many others. Note that the key difference of TST from another NLP task, style-conditioned language modeling, is that the latter is conditioned on only a style token, whereas TST takes as input both the target style attribute a and a source sentence x that constrains the content. 2020). (2020a) applied TST to generate eye-catchy headlines so they have an attractive score, and future works in this direction can also test the click-through rates. For example, Rao and Tetreault (2018) first train a phrase-based machine translation (PBMT) model on a given parallel dataset and then use back-translation (Sennrich, Haddow, and Birch 2016b) to construct a pseudo-parallel dataset as additional training data, which leads to an improvement of around 9.7 BLEU points with respect to human written references.

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