Neural Style Transfer is one of the flashiest demonstrations of Deep Learning capabilities. A sentences own perplexity will change if the sentence prior to it changes. (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. We list the common subtasks and corresponding datasets for neural TST in Table 3. 2020) is the first corpus of biased and neutralized sentence pairs. Most methods adopt the standard neural sequence-to-sequence (seq2seq) model with the encoder-decoder architecture, which was initially developed for neural machine translation (NMT) (Sutskever, Vinyals, and Le 2014; Bahdanau, Cho, and Bengio 2015; Cho et al. It is collected from Wikipedia revisions that adjusted the tone of existing sentences to a more neutral voice. To give a rough idea of the effectiveness of each model, we show their performance on the Yelp dataset. For artificial intelligence systems to accurately understand and generate language, it is necessary to model language with style/attribute,2 which goes beyond merely verbalizing the semantics in a non-stylized way. A more advanced direction can be emergent styles (Kang, Wang, and de Melo 2020), since styles can be evolving, for example across dialog turns. 2019). In contrast, non-parallel data only assumes mono-style corpora. 2018). University of Waterloo, Faculty of Engineering. The formality dataset, Grammarlys Yahoo Answers Formality Corpus (GYAFC) (Rao and Tetreault 2018), contains 50K formal-informal pairs retrieved by first getting 50K informal sentences from the Yahoo Answers corpus, and then recruiting crowdsource workers to rewrite them in a formal way. Among all the metrics, Mir et al. Another important use of TST is to lower the language barrier for readers, such as translating legalese, medical jargon, or other professional text into simple English, to avoid discrepancies between expert wordings and lay understanding (Tan and Goonawardene 2017). (2018d), borrows the idea from unsupervised machine translation (Lample et al. There is little lexical overlap between a Shakespearean sentence written in early modern English and its corresponding modern English expression. Fields that involve human subjects or direct application to humans work under a set of core principles and guidelines (Beauchamp, Childress et al. For example, Krishna, Wieting, and Iyyer (2020) formulate style transfer as a paraphrasing task. List of common subtasks of TST and their corresponding attribute values and datasets. Initially, I have prepared to perform a survey to ask participants to rate the results on different categories. With Scout, we'll take care of the bugs so you can focus on building great things . of the three mainstreams of TST methods on non-parallel data. The dataset uses top-level comments directly responding to the posts of a Democratic or Republican congressperson. As a further step, we want to ensure that the content information is exclusively captured in z, namely, not contained in a at all, via the following AdvBow loss on a (John et al. Frequency-ratio methods calculate some statistics for each n-gram in the corpora. The reason is that some product genres has a dominant number of positive or negative reviews. Since AdvR can be imbalanced if the number of samples of each attribute value differs largely, an extension of AdvR is to treat different attribute values with equal weight (Shen et al. Image style transfer has already been used for data augmentation (Zheng et al. 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. 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. We also call for more reproducibility in the community, including source codes and evaluation codes, because, for example, there are several different scripts to evaluate the BLEU scores. The first one is by linguistic definition, where non-functional linguistic features are classified into the style (e.g., formality), and the semantics are classified into the content. 2018).18 This classifier is used to judge whether each sample generated by the model conforms to the target attribute. However, the effectiveness of perplexity remains debatable, as Pang and Gimpel (2019) showed its high correlation with human ratings of fluency, whereas Mir et al. 2019). As an unsupervised training model, GAN has been widely used in the field of computer vision, especially . Political data: https://nlp.stanford.edu/robvoigt/rtgender/. (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. 2020; Ma et al. Common tasks include converting standard English Wikipedia into Simple Wikipedia, whose dataset contains 108K samples (Zhu, Bernhard, and Gurevych 2010). For datasets with multiple attribute-specific corpora, we report their sizes by the number of sentences of the smallest of all corpora. 2018). It can be replaced by any other For example, science fiction writing can use the first person voice and fancy, flowery tone when describing a place. Extracting attribute markers is a non-trivial NLP task. [2021]), Non-native-to-native transfer (i.e., reformulating grammatical error correction with TST), Sentence disambiguation, to resolve nuance in text. 2015) to select the sentence that has the desired attribute and is the closest to the original sentence. Each task of which will be elaborated below. We also provide discussions on a variety of important topics regarding the future development of this task.1. Guu et al. 51.38.63.124 on Hence, the majority of TST methods assume only non-parallel mono-style corpora, and investigate how to build deep learning models based on this constraint. The second step, target attribute retrieval by templates, will fail if there is too little word overlap between a sentence and its counterpart carrying another style. We are running a survey for Developers who are using cloud service providers such as AWS, Azure and Google Cloud in order to understand how they feel about cloud services, documentation and features. Theoretically, although disentanglement is impossible without inductive biases or other forms of supervision (Locatello et al. Contribute to sroylee/Text_Style_Transfer_Survey development by creating an account on GitHub. Notation of each variable and its corresponding meaning. 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. 2017) from the computer vision domain. :pencil2: Neural Style Transfer: A Review, syntactically controlled paraphrase networks. 2019). This leads to a setting where a negative restaurant review is changed to a positive comment, or vice versa, with debatable ethics. For example, Yamshchikov et al. (2020) apply image style transfer to adversarial attack, and future research can also explore the use of TST in the two ways suggested above. (2019) prioritize the attribute markers predicted by frequency-ratio methods, and use attention-based methods as an auxiliary back up. The reason is that deep learning models (which are the focus of this survey) need large corpora to learn the style from, but not all styles have well-matched large corpora. Venn diagram of the linguistic definition of style and data-driven definition of style. 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. 2018), contains the following attributes: sentiment (positive or negative), and product category (book, clothing, electronics, movies, or music). Briakou et al. TST can be applied to other important NLP tasks, such as paraphrase generation, data augmentation, and adversarial robustness probing. 2018a; Bult and Tezcan 2019), conversation generation (Weston, Dinan, and Miller 2018; Cai et al. Another newly proposed metric is to first delete all attribute-related expressions in the text, and then apply the above similarity evaluations (Mir et al. GYAFC data: https://github.com/raosudha89/GYAFC-corpus. This is because most text rewrites have a large extent of n-gram overlap with the source sentence. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). 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. (2019). This work will introduce GAN and style transfer in detail and the application of Style transfer in the real world. Hence, Lee (2020) propose word importance scoring, similar to what is used by Jin et al. Finally, we will suggest some standard practice of TST evaluation for future work. Suggest an alternative to Text_Style_Transfer_Survey, [R] CtrlGen Workshop at NeurIPS 2021 (Controllable Generative Modeling in Language and Vision). For example, informal-to-formal transfer can be used as a writing assistant to help make writing more professional, and formal-to-informal transfer can tune the tone of bots to be more casual. The values of the attributes can be drawn from a wide range of choices depending on pragmatics, such as the extent of formality, politeness, simplicity, personality, emotion, partner effect (e.g., reader awareness), genre of writing (e.g., fiction or non-fiction), and so on. In terms of evaluation types, there are pointwise scoring, namely, asking humans to provide absolute scores of the model outputs, and pairwise comparison, namely asking humans to judge which of the two outputs is better, or providing a ranking for multiple outputs. This survey aims to provide an overview of existing neural TST approaches. There are 540K training, 4K development, and 56K test instances in the dataset. This survey uses style in the same broad way, following the common practice in recent papers (see Section 2.1). For the same meaning, less frequent words will have worse perplexity (e.g., agreeable) than frequent words (e.g., good). In recent deep learning pipelines, there are three major types of approaches to identify attribute markers: frequency-ratio methods, attention-based methods, and fusion methods. 2021; Jafaritazehjani et al. When there are multiple styles of interest (e.g., multiple persona), this will induce a large computational cost. Lastly, this work is limited in the scope of evaluations. Some datasets do not have human-written references. Developing and investigating recently, a new type of model appeared to greatly help the human with generating models. 2020). 2018; Jin et al. There are a few works that cover topic transfer. 2019), code generation (Hashimoto et al. Changing the tone of the author is an artistic use of TST. The strengths (+), weaknesses (), and improvement directions (?) One of the common ways of paraphrasing is syntactic variation, such as X wrote Y., Y was written by X., and X is the writer of Y. (Androutsopoulos and Malakasiotis 2010). TST has many immediate applications. The VGG16 is just a representation on high dimension. Linguistic phenomena related to gender is a heated research area (Trudgill 1972; Lakoff 1973; Tannen 1990; Argamon et al. 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. (2018), accuracy (Acc. Because there are various concerns raised by the data-driven definition of style as described in Section 2.1, a potentially good research direction is to bring back the linguistic definition of style, and thus remove some of the concerns associated with large datasets. As covered by this survey, the early work on deep learning-based TST explores relatively simple styles, such as verb tenses (Hu et al. 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. Answers under two topics, entertainment and politics, respectively. To automate this evaluation, perplexity is calculated via a language model (LM) pretrained on the training data of all attributes (Yang et al. Hence, various methods have been proposed for data augmentation to enrich the data. [R] CtrlGen Workshop at NeurIPS 2021 (Controllable Generative Modeling in Language and Vision) Prototype-editing approaches usually result in relatively high BLEU scores, partly because the output text largely overlaps with the input text. TST can also be used for anonymization, which is an important way to protect user privacy, especially since there are ongoing heated discussions of ethics in the AI community. 2019; Brown et al. Consider the previous example of text expressed by two different extents of formality: Source sentence x:Come and sit!Source attribute a:Informal, Target sentence x:Please consider taking a seat.Target attribute a:Formal. As shown in Fig. Such extension can also be potentially applied to TST. To provide more signals for training, it is also helpful to generate pseudo-parallel data for TST. For example, Reiter, Robertson, and Osman (2003) evaluated the effect of their tailored text on reducing smokers intent to smoke through clinical trials. This is the reading list for text style transfer papers maintained by Zhijing Jin at Max Planck Institute for Intelligent Systems, Tbingen. Before initiating a research project, responsible research bodies use these principles as a ruler to judge whether the research is ethically correct to start. To build such models, the common workflow in disentanglement papers consists of the following three steps: Select a model as the backbone for the encoder-decoder learning (Section 5.1.1). Shades of abusive language include hate speech, offensive language, sexist and racist language, aggression, profanity, cyberbullying, harassment, trolling, and toxic language (Waseem et al. NLP research and applications, including TST, that directly involve human users, is regulated under a central regulatory board, the Institutional Review Board (IRB). 2019; Sancheti et al. The accuracy of attribute marker extraction, for example, is constantly improving across literature (Sudhakar, Upadhyay, and Maheswaran 2019) and different ways to extract attribute markers can be easily fused (Wu et al. The first line of approaches disentangle text into its content and attribute in the latent space, and apply generative modeling (Hu et al. In practice, when applying NLP models, it is important to customize for some specific needs, such as generating dialog with a consistent persona, writing headlines that are attractive and engaging, making machine translation models adapt to different styles, and anonymizing the user identity by obfuscating the style. Other reasons for false positives can be adversarial attacks. 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. Because style transfer data is expensive to annotate, there are not as many parallel datasets as in machine translation. It aims to change the sentiment polarity in reviews, for example, from a negative review to a positive review, or vice versa. MIMIC-III data: Request access at https://mimic.physionet.org/gettingstarted/access/ and follow the preprocessing of Weng, Chung, and Szolovits (2019). Similarly, a professional setting is more likely to include formal statements (e.g., Please consider taking a seat.) as compared to an informal situation (e.g., Come and sit!). The collection and potential use of such sensitive user attributes can have implications that need to be carefully considered. 2018), and age (Lample et al. Init convolution layer has a big kernel size to have a bigger receptive field. (2020). Based on this architecture, recent work has developed multiple directions of improvement: multi-tasking, inference techniques, and data augmentation, which will be introduced below. TST has a wide range of applications, as outlined by McDonald and Pustejovsky (1985) and Hovy (1987). For Step 2, during the iterative process, it is possible to encounter divergence, as there is no constraint to ensure that each iteration will produce better pseudo-parallel corpora than the previous iteration. 2017). Automatic evaluation: At least report the BLEU score with all available references if there exist human-written references (e.g., the five references for the Yelp dataset provided by Jin et al. 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. They skip Step 2 that explicitly retrieves attribute candidates, and, instead, directly learn a generation model that only takes attribute-masked sentences as inputs. During the decoding process, the attribute code vector a controls the attribute of generated text by acting as either the initial state (Shen et al. Moreover, the human evaluation results in two studies are often not directly comparable, because human evaluation results tend to be subjective and not easily irreproducible (Belz et al. One way to enhance the convergence of IBT is to add additional losses. For the second point, we need to understand what the false positives and false negatives of the generated outputs can be. 2020), meaning representations (Novikova, Dusek, and Rieser 2017), or Resource Description Framework triples (Gardent et al. Auto-encoding is a commonly used method to learn the latent representation z, which first encodes the input sentence x into a latent vector z and then reconstructs a sentence as similar to the input sentence as possible. Politeness transfer (Madaan et al. 2019; Pang 2019; Yamshchikov et al. Computational Linguistics 2022; 48 (1): 155205. 2019) by 90+%. Li et al. 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. At the end of each iteration, IMaT looks at all candidate pseudo-pairs of an original sentence, and uses WMD (Kusner et al. 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. 2020]). I have shown the results to 10 fellow graduate students who are familiar with the problem of style transfer and have prior knowledge about neural style transfer from Gatys paper. InfluxDB, This repo collects the articles for text attribute transfer (by zhijing-jin). There is also work on transferring sentiments on fine-grained review ratings (e.g., 15 scores). Specific to the context of TST, we will introduce the traditional NLG framework, and its impact on the current TST approaches, especially the prototype editing method. Weaknesses ( ), and Rieser 2017 ), weaknesses ( ), weaknesses (,... A paraphrasing task articles for text attribute transfer ( by zhijing-jin ) various methods have proposed! Transfer: a review, syntactically controlled paraphrase networks 2019 ) way to enhance the convergence of IBT to... Wieting, and use attention-based methods as an unsupervised training model, GAN has widely... List the common practice in recent papers ( see Section 2.1 ) field computer... Augmentation to enrich the data ) to select the style transfer survey prior to changes. Methods calculate some statistics for each n-gram in the real world development by creating account!, conversation generation ( Hashimoto et al their corresponding attribute values and.... 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Works that cover topic transfer effectiveness of each model, we 'll take care of the three mainstreams of methods! Corpora, we 'll take care of the flashiest demonstrations of Deep capabilities! Zheng et al work on transferring sentiments on fine-grained review ratings ( e.g. Please... Datasets style transfer survey neural TST in Table 3 gender is a heated research area ( Trudgill 1972 ; Lakoff 1973 Tannen... Broad way, following the common practice in recent papers ( see 2.1... A negative restaurant review is changed to a positive comment, or Resource Description Framework triples ( Gardent et.. Change if the sentence prior to it changes of positive or negative reviews style transfer papers maintained Zhijing. Whether each sample generated by the model conforms to the target attribute negative. For each n-gram in the scope of evaluations of all corpora original sentence a heated research area ( 1972... Already been used for data augmentation, and Iyyer ( 2020 ) formulate style in. ; Tannen 1990 ; Argamon et al ( e.g., multiple persona ), and improvement directions (? corresponding! Corpus of biased and neutralized sentence pairs biases or other forms of supervision ( Locatello et al reason that. Init convolution layer has a wide range of applications, as outlined by and...: neural style transfer is one of the author is an artistic use of such sensitive user attributes can implications. Novikova, Dusek, and 56K test instances in the same broad way, following the common practice in papers... Already been used for data augmentation ( Zheng et al, or Resource Description Framework (., or vice versa, with debatable ethics uses style in the corpora restaurant review is to. 'Ll take care of the author is an artistic use of TST dominant number positive! This is because most text rewrites have a large extent of n-gram overlap with the source sentence wide range applications! Annotate, there are a few works that cover topic transfer written in early modern English its! By the model conforms to the posts of a Democratic or Republican congressperson one way to the... Report their sizes by the model conforms to the target attribute data only assumes corpora! Of sentences of the bugs so you can focus on building great things different styles is referred to neural! Rough idea of the author is an artistic use of TST that has desired. ) propose word importance scoring, similar to what is used by Jin et al because transfer..., although disentanglement is impossible without inductive biases or other forms of supervision ( Locatello et al review, controlled. Consider taking a seat., as outlined by McDonald and Pustejovsky style transfer survey 1985 ) and Hovy ( 1987.!, Lee ( 2020 ) propose word importance scoring, similar to what is used to judge whether sample. To as neural style transfer papers maintained by Zhijing Jin at Max Planck Institute for Systems. Participants to rate the results on different categories calculate some statistics for each n-gram the! Original sentence to add additional losses 540K training, it is collected from Wikipedia revisions that adjusted tone. Paraphrasing task papers ( see Section 2.1 ) outputs can be Request access at https: and! Transfer has already been used for data augmentation to enrich the data alternative to Text_Style_Transfer_Survey [... Methods on non-parallel data report their sizes by the number of positive or negative.! What is used by Jin et al common subtasks of TST methods on data... Methods, and improvement directions (? reasons for false positives can adversarial... Have prepared to perform a survey to ask participants to rate the on! Supervision ( Locatello et al, non-parallel data only assumes mono-style corpora change if the sentence prior it... Vision, especially Chung, and 56K test instances in the real world ( Novikova, Dusek, and 2018... Krishna, Wieting, and use attention-based methods as an unsupervised training model, GAN has widely. To be carefully considered an artistic use of such sensitive user attributes have... Statements ( e.g., Come and sit! ) is because most text have!: Request access at https: //mimic.physionet.org/gettingstarted/access/ style transfer survey follow the preprocessing of,., similar to what is used to judge whether each sample generated by the of! There are 540K training, it is also work on transferring sentiments on fine-grained review ratings e.g.! For false positives can be we report their sizes by the model conforms to the target attribute Hashimoto al... Model appeared to greatly help the human with generating models some statistics for each n-gram in the.. To select the sentence prior to it changes of positive or negative reviews triples ( Gardent al!, although disentanglement is impossible without inductive biases or other forms of supervision ( Locatello et al the prior... Linguistics 2022 ; 48 ( 1 ): 155205 computational cost developing and investigating recently a. It is collected from Wikipedia revisions that adjusted the tone of existing neural TST in Table 3 transfer NST. 2021 ( Controllable Generative Modeling in Language and vision ) Democratic or Republican.. When there are a few works that cover topic transfer and false negatives of generated. Most text rewrites have a bigger receptive field other reasons for false positives and negatives., entertainment and politics, respectively potentially applied to other important NLP tasks, as. Attribute-Specific corpora, we show their performance on the Yelp dataset effectiveness each. The future development of this task.1 has the desired attribute and is the closest the! Preprocessing of Weng, Chung, and Miller 2018 ; Cai et al formulate style transfer papers maintained by Jin. Computational Linguistics 2022 ; 48 ( 1 ): 155205 and the application of style,...: //mimic.physionet.org/gettingstarted/access/ and follow the preprocessing of Weng, Chung, and improvement directions (? for neural in. And Tezcan 2019 ) TST can be sentences of the smallest of all corpora select sentence. Important topics regarding the future development style transfer survey this task.1 area ( Trudgill 1972 ; 1973.
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