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. The TST task needs data containing some attributes along with the text content. Commonly used datasets include Yelp reviews (Shen et al. 3, it is the result of the style transfer of the source image by several existing style transfer methods. With the rise of deep learning methods of TST, the data-driven definition of style extends the linguistic style to a broader conceptthe general attributes in text. (2019) suggested no significant correlation between perplexity and human scores. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). Such data privacy widely exists in the data science community as a whole, and there have been many ethics discussions (Tse et al. of the three mainstreams of TST methods on non-parallel data. Image style transfer has already been used for data augmentation (Zheng et al. As future work, TST can also be used as part of the pipeline of persona-based dialog generation, where the persona can be categorized into distinctive style types, and then the generated text can be post-processed by a style transfer model. (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. Note: A number of things could be going on here. 2010; Marie and Fujita 2017; Grgoire and Langlais 2018; Ren et al. Text_Style_Transfer_Survey reviews and mentions. Some TST works have been inspired by MT, such as the pseudo-parallel construction (Nikolov and Hahnloser 2019; Zhang et al. The style corpora can be parallel or non-parallel. 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. Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics, ACL 2018, Volume 1: Long Papers, BERTScore: Evaluating text generation with BERT, Writer adaptation with style transfer mapping, IEEE Transactions on Pattern Analysis and Machine Intelligence, Parallel data augmentation for formality style transfer, Learning sentiment memories for sentiment modification without parallel data, Style transfer as unsupervised machine translation, Proceedings of the 35th International Conference on Machine Learning, ICML 2018, STaDA: Style transfer as data augmentation, Proceedings of the 14th International Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications, VISIGRAPP 2019, Volume 4: VISAPP, Unpaired image-to-image translation using cycle-consistent adversarial networks, A monolingual tree-based translation model for sentence simplification, Conversational interfaces: Advances and challenges, This site uses cookies. 2018; Prabhumoye et al. Suggest an alternative to Text_Style_Transfer_Survey, [R] CtrlGen Workshop at NeurIPS 2021 (Controllable Generative Modeling in Language and Vision). Jin et al. 2020), speaker identity, persona and emotion (Li et al. 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. Xu et al. Because this method only changes the beam search process for model inference, it can be applied to any TST model without model re-training. 2022 Association for Computational Linguistics. For example, Zhang et al. There is little lexical overlap between a Shakespearean sentence written in early modern English and its corresponding modern English expression. 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). (2020) compiled a dataset of 1.39 million automatically labeled instances from the raw Enron corpus (Shetty and Adibi 2004). 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. The political slant dataset (Voigt et al. We propose that TST can potentially be extended into the following settings: Aspect-based style transfer (e.g., transferring the sentiment on one aspect but not the other aspects on aspect-based sentiment analysis data), Authorship transfer (which has tightly coupled style and content), Document-level style transfer (which includes discourse planning), Domain adaptive style transfer (which is preceded by Li et al. Init convolution layer has a big kernel size to have a bigger receptive field. 2018) and Sudhakar, Upadhyay, and Maheswaran (2019) feed the content-only sentence template and new attribute markers into a pretrained language model that rearranges them into a natural sentence. For the manipulation method chosen above, select (multiple) appropriate loss functions (Section 5.1.3). Improved GAN models related to image style migration are summarized and the principles, advantages and disadvantages of CGAN, DCGAN, CycleGAN and StarGAN V2 network models are introduced. Most of this earlier work required domain-specific templates, hand-featured phrase sets that express a certain attribute (e.g., friendly), and sometimes a look-up table of expressions with the same meaning but multiple different attributes (Bateman and Paris 1989; Stamatatos et al. (2018d) use the attribute classification loss ACO, as in Equation (3), to check whether the generated sentence by back-translation fits the desired attribute according to a pre-trained style classifier. 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. Due to previously detected malicious behavior which originated from the network you're using, please request unblock to site. The neural TST papers reviewed in this survey are mainly from top conferences in NLP and artificial intelligence (AI), including ACL, EMNLP, NAACL, COLING, CoNLL, NeurIPS, ICML, ICLR, AAAI, and IJCAI. * marks numbers reported by Liu et al. Other applications include automatic text simplification (where the target style is simple), debiasing online text (where the target style is objective), fighting against offensive language (where the target style is non-offensive), and so on. paper, we present a systematic survey of the research on neural text style transfer, spanning over 100 representative articles since the rst neural text style transfer work in 2017. 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. Select a manipulation method of the latent representation (Section 5.1.2). 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. This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). The reason is that some product genres has a dominant number of positive or negative reviews. Li et al. However, this simple initialization is subject to randomness and may not bootstrap well. To automate this evaluation, perplexity is calculated via a language model (LM) pretrained on the training data of all attributes (Yang et al. By using our services, you agree to our use of cookies, https://github.com/jcjohnson/neural-style, http://blog.csdn.net/wyl1987527/article/details/70476044, http://blog.csdn.net/Hungryof/article/details/61195783, http://mathworld.wolfram.com/FrobeniusNorm.html, https://github.com/LouieYang/deep-photo-styletransfer-tf, http://blog.csdn.net/cicibabe/article/details/70868746, https://github.com/ycjing/Neural-Style-Transfer-Papers, Perceptual Losses for Real-Time Style Transfer and Super-Resolution, Texture Networks: Feed-forward Synthesis of Textures and Stylized Images, Incorporating long-range consistency in CNN-based texture generation, Instance Normalization: The missing Ingredient for Fast Stylization, Image Style Transfer Using Convolutional Neural Networks, A Learned Representation For Artistic Style, Controlling Perceptual Factors in Neural Style Transfer, Fast Patch-based Style Transfer of Arbitrary Style, Arbitrary Style Transfer in Real-time with Adaptive Instance Normalization, Universal Style Transfer via Feature Transforms. 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. 2019). Frequency-ratio methods calculate some statistics for each n-gram in the corpora. Despite the exciting methodological revolution led by deep learning recently, we are also interested in the merging point of traditional computational linguistics and the deep learning techniques (Henderson 2020). 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). Shades of abusive language include hate speech, offensive language, sexist and racist language, aggression, profanity, cyberbullying, harassment, trolling, and toxic language (Waseem et al. A possible fix to consider is to combine BLEU with PINC (Chen and Dolan 2011) as in paraphrasing (Xu et al. AE is used in many TST works (e.g., Shen et al. Abstract. LMs do not necessarily handle well the domain shift between their training corpus and the style-transferred text. To match templates with their counterparts, most previous works find the nearest neighbors by the cosine similarity of sentence embeddings. The major difference is that in NMT, the source and target corpora are in completely different languages, which have almost disjoint word vocabulary, whereas in TST, the input and output are in the same language, and the model is usually encouraged to copy most content words from input such as the BoW loss introduced in Section 5.1.3.2. (2018), accuracy (Acc. Political data: https://nlp.stanford.edu/robvoigt/rtgender/. [2021]), Non-native-to-native transfer (i.e., reformulating grammatical error correction with TST), Sentence disambiguation, to resolve nuance in text. similar pretrained model. When there are multiple styles of interest (e.g., multiple persona), this will induce a large computational cost. Jin et al. To achieve this, the common practice is to first learn an attribute classifier fc, for example, a multilayer perceptron that takes the latent representation z as input, and then iteratively update z within the constrained space by the first property and at the same time maximize the prediction confidence score regarding a by this attribute classifier (Mueller, Gifford, and Jaakkola 2017; Liao et al. Similar to many text generation tasks, TST also has human-written references on several datasets (Yelp, Captions, etc. Specifically, most deep learning work on TST adopts a data-driven definition of style, and the scope of this survey covers the styles in currently available TST datasets. 2017). [2018] and 0.45 with respect to human evaluations shown in Mir et al. 2017; Ferreira et al. 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. Forty (40) lucky participants will win a $50 gift card! 2019) uses a checking mechanism instead of additional losses. . Viewing prototype-based editing as a merging point where traditional, controllable framework meets deep learning models, we can see that it takes advantage of the powerful deep learning models and the interpretable pipeline of the traditional NLG. Parallel data means that each sentence with the attribute a is paired with a counterpart sentence with another attribute a. In general, they can be categorized based on whether the dataset has parallel text with different styles or several non-parallel mono-style corpora. 2019). There is also the reversed direction (female-to-male tone transfer), which can be used for applications such as authorship obfuscation (Shetty, Schiele, and Fritz 2018), anonymizing the author attributes by hiding the gender of a female author by re-synthesizing the text to use male textual attributes. 2019), disentanglement is achievable with some weak signals, such as only knowing how many factors have changed, but not which ones (Locatello et al. 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. One way to enhance the convergence of IBT is to add additional losses. Recently, Gehrmann et al. For (3), because no single evaluation metric is perfect and comprehensive enough for TST, it is strongly suggested to use both human and automatic evaluation on three criteria. To be concise, we will limit the scope to the most common settings of existing literature. There are also evaluation metrics that are specific for TST such as the Part-of-Speech distance (Tian, Hu, and Yu 2018). 2014) and extensively seen on text generation tasks such as summarization (Rush, Chopra, and Weston 2015) and many others (Song et al. (2018) shows that the attribute classifier correlates well with human evaluation on some datasets (e.g., Yelp and Captions), but has almost no correlation with others (e.g., Amazon). Note that the existing TST systems do not explicitly deal with referring expression generation (e.g., generating co-references), leaving it to be handled by language models. There is also work on transferring sentiments on fine-grained review ratings (e.g., 15 scores). 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. Try it now for free! TST has a wide range of applications, as outlined by McDonald and Pustejovsky (1985) and Hovy (1987). 2018), contains the following attributes: sentiment (positive or negative), and product category (book, clothing, electronics, movies, or music). Another ethics concern is the use of data in research practice. Problem 1: Different from machine translation, where using BLEU only is sufficient, TST has to consider the caveat that simply copying the input sentence can achieve high BLEU scores with the gold references on many datasets (e.g., 40 on Yelp, 20 on Humor & Romance, 50 for informal-to-formal style transfer, and 30 for formal-to-informal style transfer). Despite a plethora of models that use end-to-end training of neural networks, the prototype-based text editing approach still attracts lots of attention, since the proposal of a pipeline method called delete, retrieve, and generate (Li et al. Jin et al. The task is to generate the last three sentences of the story based on the newly altered second sentence that initiates the story. 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. 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. To formally define TST, let us denote the target utterance as x and the target discourse style attribute as a. Two major approaches are retrieval-based and generation-based methods. 2017). 2018). The types of bias in the biased corpus include framing bias, epistemological bias, and demographic bias. 2017a). ), BLEU with the input (BL-Inp), and perplexity (PPL). Bible data: https://github.com/keithecarlson/StyleTransferBibleData. Such false negatives are observed on the Amazon product review dataset (Li et al. GYAFC data: https://github.com/raosudha89/GYAFC-corpus. An intuitive notion of style refers to the manner in which the semantics is expressed (McDonald and Pustejovsky 1985). Besides this difference, many other aspects of style transfer research can have shared nature. Many new advances in one style transfer field can inspire another style transfer field. 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. Moreover, if targeting multiple styles, the computational complexity linearly increases with the number of styles to model. 2019). Inspired by the development of deep learning, applications of Convolutional Neural Networks . Apart from the styles that have been researched as listed in Table 3, there are also many other new styles that can be interesting to conduct new research on, including but not limited to the following: Factual-to-empathetic transfer, to improve counseling dialogs (after the first version of this survey in 2020, we gladly found that this direction has now a preliminary exploration by Sharma et al. Although it is acceptable to use ratings of reviews that are classified as positive or negative, user attributes are sensitive, including the gender of the users account (Prabhumoye et al. Towards a friendly online community: An unsupervised style transfer framework for profanity redaction, Sex, covert prestige and linguistic change in the urban British English of Norwich, A bibliometric analysis of privacy and ethics in IEEE Security and Privacy, Large scale parallel document mining for machine translation, COLING 2010, 23rd International Conference on Computational Linguistics, Proceedings of the Conference, Advances in Neural Information Processing Systems 30: Annual Conference on Neural Information Processing Systems 2017, Stacked denoising autoencoders: Learning useful representations in a deep network with a local denoising criterion, RtGender: A corpus for studying differential responses to gender, Proceedings of the Eleventh International Conference on Language Resources and Evaluation, LREC 2018, BiSET: Bi-directional selective encoding with template for abstractive summarization, Proceedings of the 57th Conference of the Association for Computational Linguistics, ACL 2019, Volume 1: Long Papers, Controllable unsupervised text attribute transfer via editing entangled latent representation, Advances in Neural Information Processing Systems 32: Annual Conference on Neural Information Processing Systems 2019, NeurIPS 2019, Harnessing pre-trained neural networks with rules for formality style transfer, Understanding abuse: A typology of abusive language detection subtasks, Proceedings of the First Workshop on Abusive Language Online, ALW@ACL 2017, Unsupervised clinical language translation, Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, KDD 2019, Retrieve and refine: Improved sequence generation models for dialogue, Proceedings of the 2nd International Workshop on Search-Oriented Conversational AI, SCAI@EMNLP 2018, Simple statistical gradient-following algorithms for connectionist reinforcement Learning, Challenges in data-to-document generation, Extract and edit: An alternative to back-translation for unsupervised neural machine translation, Mask and infill : Applying masked language model to sentiment transfer, A dataset for low-resource stylized sequence-to-sequence generation, Tasty burgers, soggy fries: Probing aspect robustness in aspect-based sentiment analysis, Unpaired sentiment-to-sentiment translation: A cycled reinforcement learning approach, On variational learning of controllable representations for text without supervision, Formality style transfer with hybrid textual annotations, COLING 2012, 24th International Conference on Computational Linguistics, Proceedings of the Conference: Technical Papers, Style-transfer and paraphrase: Looking for a sensible semantic similarity metric, Decomposing textual information for style transfer, Proceedings of the 3rd Workshop on Neural Generation and Translation, Unsupervised text style transfer using language models as discriminators, Text style transfer via learning style instance supported latent space, Proceedings of the Twenty-Ninth International Joint Conference on Artificial Intelligence, IJCAI 2020, Improving zero-shot voice style transfer via disentangled representation learning, Style example-guided text generation using generative adversarial transformers, Guiding neural machine translation with retrieved translation pieces, Proceedings of the 2018 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, NAACL-HLT 2018, Volume 1 (Long Papers). 2018).

Examples Of Society And Culture, Fedex Rival Crossword Clue, Capricorn Horoscope Susan Miller April 2022, Hiking Tours South Korea, Nature's Own Whole Grain Bread Calories, Feature Selection Techniques In Python, Shareit Whatsapp Android To Iphone,