2020). So the two tasks are not completely the same, although they have some intersections that might inspire future work, such as aspect-based style transfer suggested in Section 6.1. To ease the understanding for the reader, we will in most cases explain TST on one attribute between two values, such as transferring the formality between informal and formal tones, which can potentially be extended to multiple attributes. 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. Specifically for formality transfer, Zhang, Ge, and Sun (2020) multi-task TST and grammar error correction (GEC) so that knowledge from GEC data can be transferred to the informal-to-formal style transfer task. Previously, this has been done by encoding speaker traits into a vector and the conversation is then conditioned on this vector (Li et al. For example, early work in NLG for weather forecasts builds domain-specific templates to express different types of weather with different levels of uncertainty for different users (Sripada et al. It is an approach to generating full images in an artistic style from line drawings. For example, a Republicans comment can be defund all illegal immigrants, while Democrats are more likely to support humanistic actions towards immigrants. Forty (40) lucky participants will win a $50 gift card! Jin et al. 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. (2019) and Yamshchikov et al. 2019). This article presented a comprehensive review of TST with deep learning methods. For evaluation, so far researchers have allowed the human judges to decide the scores of transferred style strength and the content preservation. 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. 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. Note that we interchangeably use the terms style and attribute in this 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. The last one was on 2021-08-30. Neural Style Transfer is one of the flashiest demonstrations of Deep Learning capabilities. For example, Could you please send me the data? is a more polite expression than send me the data!. 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. Because the majority of TST research focuses on non-parallel data, we discuss here its strengths and limitations. First, TST can be used to help other NLP tasks such as paraphrasing, data augmentation, and adversarial robustness probing (Section 7.1). Each of the three losses can gain performance improvement of 15 BLEU points with the human references (Xu, Ge, and Wei 2019). 2020; Guo et al. Some datasets do not have human-written references. 2005; Belz 2008; Gkatzia, Lemon, and Rieser 2017). Other reasons for false positives can be adversarial attacks. Traditional approaches rely on term replacement and templates. An illustrative example is that if the style classifier only reports 80+% performance (e.g., on the gender dataset [Prabhumoye et al. Abstract. 2018; Dai et al. Li et al. For example, someone who is uncertain is more likely to use tag questions (e.g., This is true, isnt it?) than declarative sentences (e.g., This is definitely true.). We will first introduce the practice of automatic evaluation on the three criteria, discuss the benefits and caveats of automatic evaluation, and then introduce human evaluation as a remedy for some of the intrinsic weaknesses of automatic evaluation. 2018; Wang, Hua, and Wan 2019; Liu et al. 2018) used in Sudhakar, Upadhyay, and Maheswaran (2019). Such a checklist-based evaluation can make the performance of black-box deep learning models more interpretable, and also allow for more insightful error analysis. Extracting attribute markers is a non-trivial NLP task. 2018). For example, at least (1) experiment on at least one commonly used dataset, (2) list up-to-date best-performing previous models as baselines, (3) report on a superset of the most commonly used metrics, and (4) release system outputs. An alternative way to achieve the second property is to multi-task by another auto-encoding task on the corpus with the attribute a and share most layers of the transformer except the query transformation and layer normalization layers (Jin et al. 2010) to replace AE in NLP tasks. 2017; Hu et al. GYAFC data: https://github.com/Elbria/xformal-FoST. Due to previously detected malicious behavior which originated from the network you're using, please request unblock to site. Alternatively, IMaT (Jin et al. 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. Beyond the intrinsic personal styles, for pragmatic uses, style further becomes a protocol to regularize the manner of communication. The VGG16 is just a representation on high dimension. Sonar Therefore, the commonly used practice of evaluation considers the following three criteria: (1) transferred style strength, (2) semantic preservation, and (3) fluency. 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. To match templates with their counterparts, most previous works find the nearest neighbors by the cosine similarity of sentence embeddings. similar pretrained model. Try it now for free! Another model, Iterative Matching and Translation (IMaT) (Jin et al. 2020). 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. We have surveyed recent research efforts in TST and developed schemes to categorize and distill the existing literature. Artificial Intelligence 72 TST aims to model p(x|a, x), where x is a given text carrying a source attribute value a. Note that there are style transfer works across different modalities, including images (Gatys, Ecker, and Bethge 2016; Zhu et al. 3, it is the result of the style transfer of the source image by several existing style transfer methods. Initially, I have prepared to perform a survey to ask participants to rate the results on different categories. In this section, we will introduce three main branches of TST methods: disentanglement (Section 5.1), prototype editing (Section 5.2), and pseudo-parallel corpus construction (Section 5.3). (2019). As politeness is culture-dependent, this dataset mainly focuses on politeness in North American English. [R] CtrlGen Workshop at NeurIPS 2021 (Controllable Generative Modeling in Language and Vision) Contents: Papers; Practice; Paper Reading Notes; There are several advantages in merging the traditional NLG with the deep learning models. Fusion methods combine the advantages of the above two methods. The first approach, Latent Representation Editing (LRE), shown in Figure 2a, is achieved by ensuring two properties of the latent representation z. Scout APM To be concise, we will limit the scope to the most common settings of existing literature. Specifically, image style transfer technique is to specify an input image as the base image, also known as the content image. (2020) compiled a dataset of 1.39 million automatically labeled instances from the raw Enron corpus (Shetty and Adibi 2004). Humor and romance are some artistic attributes that can provide readers with joy. Once this technology is used, it will automatically manipulate the online text to contain polarity that the model owner desires. 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). Hence, they construct the initial pseudo corpora by matching sentence pairs in the two attributed corpora according to the cosine similarity of pretrained sentence embeddings. Last but not least, we overview the ethical impacts that are important to take into consideration for future development of TST (Section 7.3). Another task is to simplify medical descriptions to patient-friendly text, including a dataset with 2.2K samples (den Bercken, Sips, and Lofi 2019), another non-parallel dataset with 59K free-text discharge summaries compiled from MIMIC-III (Weng, Chung, and Szolovits 2019), and a more recent parallel dataset with 114K samples compiled from the health reference Merck Manuals (MSD), where discussions on each medical topic has one version for professionals, and the other for consumers (Cao et al. Such extension can also be potentially applied to TST. Over the last several years, various methods have been proposed for TST. For Step 1, in order to generate the initial pseudo-parallel corpora, a simple baseline is to randomly initialize the two models Maa and Maa, and use them to translate the attribute of each sentence in x X and x X. Many Git commands accept both tag and branch names, so creating this branch may cause unexpected behavior. Association for Computational Linguistics. Wiki Neutrality data: http://bit.ly/bias-corpus. Compared with the pros and cons of the automatic evaluation metrics mentioned above, human evaluation stands out for its flexibility and comprehensiveness. This is a dataset commonly used for benchmarks in computer vision tasks. BLEU is shown to have low correlation with human evaluation. (2020) form a two-topic corpus by compiling Yahoo! Commonly used sentence embeddings include TF-IDF as used in Li et al. We also provide several guidelines below to avoid ethical misconduct in future publications on TST. (2020) apply image style transfer to adversarial attack, and future research can also explore the use of TST in the two ways suggested above. Notation of each variable and its corresponding meaning. MSD data: https://srhthu.github.io/expertise-style-transfer/. (2019) use a MLM of the template conditioned on the target attribute, and this MLM is trained on an additional attribute classification loss using the model output and a fixed pre-trained attribute classifier. We thank Qipeng Guo for his insightful discussions and the anonymous reviewers for their constructive suggestions. There are two phenomena rising from the data-driven definition of style as opposed to the linguistic style. The traditional NLG framework stages sentence generation into the following steps (Reiter and Dale 1997): The first two steps, content determination and discourse planning, are not applicable to most datasets because the current focus of TST is sentence-level and not discourse-level. A sentences own perplexity will change if the sentence prior to it changes. 2019), so it can be interesting to see future works also apply TST for data augmentation. 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. InfluxDB, This repo collects the articles for text attribute transfer (by zhijing-jin). This process of using CNNs to render a content image in different styles is referred to as Neural Style Transfer (NST). 2017), many recent dataset collection works automatically look for meta-information to link a corpus to a certain attribute. AE is used in many TST works (e.g., Shen et al. There is little lexical overlap between a Shakespearean sentence written in early modern English and its corresponding modern English expression. (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. 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. 2020), and so on. To focus on content information only, John et al. (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. 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. 2017). We list the common subtasks and corresponding datasets for neural TST in Table 3. 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? 2020a), stylized language modeling to imitate specific authors (Syed et al. Finally, Section 2.3 lists all the common subtasks for neural TST which can save the literature review efforts for future researchers. 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. 2019). For example, take one of the most popular TST tasks, sentiment modification; although it can be used to change intelligent assistants or robots from a negative to positive mood (which is unlikely to harm any parties), the vast majority of research applies this technology to manipulate the polarity of reviews, such as Yelp (Shen et al. 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. Style transfer is a task to recompose the content of an image in the style of another. Other than conference papers, we also include some non-peer-reviewed preprint papers that can offer some insightful information about the field. 2019). To alleviate expensive human labor, Xing et al. 2018). Contents: Papers Practice Paper Reading Notes Code Myself References Papers A Neural Algorithm of Artistic Style arxiv: 1508.06576 github: https://github.com/jcjohnson/neural-style translation: https://www.jianshu.com/p/9f03b61fdeac
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