On the other hand, the biases of the LM correlate with sentence length, synonym replacement, and prior context. 2018; Gong et al. Prototype-editing approaches usually result in relatively high BLEU scores, partly because the output text largely overlaps with the input text. It can be trained with both supervised or unsupervised learning and contain both generative and discriminative models. Another important use of TST is to fight against offensive language. 2010; Marie and Fujita 2017; Grgoire and Langlais 2018; Ren et al. 2019). Linguistic phenomena related to gender is a heated research area (Trudgill 1972; Lakoff 1973; Tannen 1990; Argamon et al. 2018). This survey has covered the task formulation, evaluation metrics, and methods on parallel and non-parallel data. The method to construct pseudo-parallel data can be effective, especially when the pseudo-parallel corpora resemble supervised data. (2019), Amazon data preprocessed by Li et al. Attribute is a broader terminology that can include content preferences, e.g., sentiment, topic, and so on. 2019). Recently, there is more and more attention being paid to the ethics concerns associated with AI research. 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. Denote the sentence template with all attribute markers deleted as Template(x) =x\Markera(x). 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.). For example, Could you please send me the data? is a more polite expression than send me the data!. TST can serve as a very helpful tool as it can be used to transfer malicious text to normal language. (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. 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. TST has many immediate applications. Prototype editing is reminiscent of early word replacement methods used for TST, such as synonym matching using a style dictionary (Sheikha and Inkpen 2011), WordNet (Khosmood and Levinson 2010; Mansoorizadeh et al. For example, Wu, Wang, and Liu (2020) translate from informal Chinese to formal English. The first line of approaches disentangle text into its content and attribute in the latent space, and apply generative modeling (Hu et al. 2019; Bao et al. For (4), it will also be very helpful to provide system outputs for each TST paper, so that future works can better reproduce both human and automatic evaluation results. Recently, some additional deep-learning-based metrics have been proposed, such as cosine similarity based on sentence embeddings (Fu et al. To avoid the model copying too many parts of the input sentence and not performing sufficient edits to flip the attribute, Kajiwara (2019) first identifies words in the source sentence requiring replacement, and then changes the words by negative lexically constrained decoding (Post and Vilar 2018) that avoids naive copying. 2016; See, Liu, and Manning 2017) is also added to better handle stretches of text that should not be changed (e.g., some proper nouns and rare words) (Gu et al. Dataset. 2019; Fu et al. [2019]). 2018), and BERTScore (Zhang et al. 2019; Dathathri et al. 2018a) that first learns an unsupervised word-to-word translation table between attribute a and a, and uses it to generate an initial pseudo-parallel corpora. 2020). 2018).18 This classifier is used to judge whether each sample generated by the model conforms to the target attribute. Because these generation practices are complicated, Madaan et al. How do you feel about the Cloud service providers you use?. In contrast, the second practice is data-drivengiven two corpora (e.g., a positive review set and a negative review set), the invariance between the two corpora is the content, whereas the variance is the style (e.g., sentiment, topic) (Mou and Vechtomova 2020). To approximately measure content preservation, bag-of-words (BoW) features are used by John et al. 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. Madaan et al. Moreover, the existing datasets can decouple style and style-independent contents relatively well. 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). The advantage of the data-driven style is that it can marry well with deep learning methods because most neural models learn the concept of style by learning to distinguish the multiple style corpora. (2020) first calculate the ratio of mean TF-IDF between the two attribute corpora for each n-gram, then normalize this ratio across all possible n-grams, and finally mark those n-grams with a normalized ratio p higher than a pre-set threshold as attribute markers. The two are combined to get the final result, broadly applied in social communication, animation production, entertainment items. 2020), it is common to formulate data-to-text as a seq2seq task by serializing the structured data into a sequence (Kale and Rastogi 2020; Ribeiro et al. 2020), but TST has not yet been applied for such usage. Another way is through generation, such as iterative back-translation (IBT) (Hoang et al. Wiki Neutrality data: http://bit.ly/bias-corpus. This assumption can potentially be loosened in two ways. TST has a wide range of applications, as outlined byMcDonald and Pustejovsky(1985) andHovy(1987). A commonly used fix is to make the evaluation more fine-grained using three different independent aspects, namely, transferred style strength, semantic preservation, and fluency, which will be detailed below. Beyond the intrinsic personal styles, for pragmatic uses, style further becomes a protocol to regularize the manner of communication. (2021) use cycle training with a conditional variational auto-encoder to unsupervisedly learn to express the same semantics through different styles. For example, Wu et al. One is that the data-driven definition of style can include a broader range of attributes including content and topic preferences of the text. The second property is that z should be learned such that it incorporates the new attribute value of interest a. You signed in with another tab or window. (2018d), borrows the idea from unsupervised machine translation (Lample et al. 2018d; Jin et al. 2018; Prabhumoye et al. Commonly used styles for TST in machine translation are politeness (Sennrich, Haddow, and Birch 2016a) and formality (Niu, Martindale, and Carpuat 2017; Wu, Wang, and Liu 2020). of the three mainstreams of TST methods on non-parallel data. University of Michigan, EECS, College of Engineering. For example, Zhang et al. In this case, a TST model should be able to modify the formality and generate the formal sentence x = Please consider taking a seat. given the informal input x = Come and sit!. It aims to change the sentiment polarity in reviews, for example, from a negative review to a positive review, or vice versa. Deep Learning for Text Style Transfer: A Survey takes as input both the target style attribute a0and a source sentence x that constrains the content. 2020). (2019) propose a more challenging setting of text attribute transfer: multi-attribute transfer. 2018). Re-train these two style transfer models on the datasets generated by 1, that is, re-train Maa(x) on (x, x) pairs and Maa(x) on (x, x) pairs. 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. 2018; Prabhumoye et al. Before initiating a research project, responsible research bodies use these principles as a ruler to judge whether the research is ethically correct to start. 2019) uses a checking mechanism instead of additional losses. Section 2.2 gives a task formulation and introduces the notations that will be used across the survey. There are more and more efforts put into combating toxic language, such as 30K content moderators that Facebook and Instagram employ (Harrison 2019). 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. : Conf. There is also other negative text such as propaganda (Bernays 2005; Carey 1997), and others. This infilling process can naturally be achieved by a masked language model (MLM) (Malmi, Severyn, and Rothe 2020). 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. TST is a good method for data augmentation because TST can produce text with different styles but the same meaning. Retrieve candidate attribute markers carrying the desired attribute a (Section 5.2.2). In addition to the seq2seq learning on paired attributed-text, Xu, Ge, and Wei (2019) propose adding three other loss functions: (1) classifier-guided loss, which is calculated using a well-trained attribute classifier and encourages the model to generate sentences conforming to the target attribute, (2) self-reconstruction loss, which encourages the seq2seq model to reconstruct the input itself by specifying the desired style the same as the input style, and (3) cycle loss, which first transfers the input sentence to the target attribute and then transfers the output back to its original attribute. This prototype-and-then-edit approach can also be seen in summarization (Wang, Quan, and Wang 2019), machine translation (Cao and Xiong 2018; Wu, Wang, and Wang 2019; Gu et al. One use case is when frequency-ratio methods fail to identify any attribute markers in a given sentence, they will use the attention-based methods as a secondary choice to generate attribute markers. Posts with mentions or reviews of Text_Style_Transfer_Survey . 2019), code generation (Hashimoto et al. 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. An illustrative example is that if the style classifier only reports 80+% performance (e.g., on the gender dataset [Prabhumoye et al. This is an open-access article distributed under the terms of the, Instead of reconstructing data based on the deterministic latent representations by AE, a variational auto-encoder (VAE) (Kingma and Welling, ACO aims to make sentences generated by the generator, Different from the previous ACO objective, whose training signal is from the output sentence, As the previous ACR explicitly requires the latent. Using a similar approach, Madaan et al. Over the last few years, many novel TST algorithms have been developed, while the industry has leveraged these algorithms to enable exciting TST applications. 2021). 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). LibHunt tracks mentions of software libraries on relevant social networks. As pre-trained models became prevalent in recent years, the DAE training method has increased in popularity relative to its counterparts such as GAN and VAE, because pre-training over large corpora can grant models better performance in terms of semantic preservation and fluency (Lai, Toral, and Nissim 2021; Riley et al. As common practice, most works use 100 outputs for each style transfer direction (e.g., 100 outputs for formal informal, and 100 outputs for informal formal), andtwo human annotators for each task (Shen et al. MIMIC-III data: Request access at https://mimic.physionet.org/gettingstarted/access/ and follow the preprocessing of Weng, Chung, and Szolovits (2019). 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. Note that there are style transfer works across different modalities, including images (Gatys, Ecker, and Bethge 2016; Zhu et al. Among the approaches introduced so far, the most relevant for the traditional NLG is the prototype-based text editing, which has been introduced in Section 5.2. Image style transfer has already been used for data augmentation (Zheng et al. 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!). 2018; Li et al. We analyze the three major streams of approaches for unsupervised TST in Table 6, including their strengths, weaknesses, and future directions. 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. Lastly, this work is limited in the scope of evaluations. 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 original semantics, and maintains natural language fluency. 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. 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. (2020a) first use TST to generate eye-catchy headlines with three different styles: humorous, romantic, and clickbaity styles. We also discussed several important topics in the research agenda of TST, and how to expand the impact of TST to other tasks and applications, including ethical considerations. 2019), disentanglement is achievable with some weak signals, such as only knowing how many factors have changed, but not which ones (Locatello et al. 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. However, the well-known limitations of human evaluation are cost and irreproducibility. Style transfer means using a neural network to extract the content of one image and the style of the other image. 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. The pretrained models are imperfect in the sense that they will favor toward a certain type of methods. 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. For datasets with multiple attribute-specific corpora, we report their sizes by the number of sentences of the smallest of all corpora. Traditional ways to do it involve first using tagging, parsing, and morphological analysis to select features, and then filtering by mutual information and chi-square testing. The TST task needs data containing some attributes along with the text content. The style of language is crucial because it makes natural language processing more user-centered. And this technology is widely used in all aspects of life. Yahoo! 2018a; Bult and Tezcan 2019), conversation generation (Weston, Dinan, and Miller 2018; Cai et al. As covered by this survey, the early work on deep learning-based TST explores relatively simple styles, such as verb tenses (Hu et al. Given the advances in TST methodologies, it now starts to expand its impact to downstream applications, such as persona-based dialog generation (Niu and Bansal 2018; Huang et al. 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. There are three implications of this connection of TST and paraphrase generation. To avoid auto-encoding from blindly copying all the elements from the input, Hill, Cho, and Korhonen (2016) adopt denoising auto-encoding (DAE) (Vincent et al. Cookies help us deliver our services. 2017) and deep learning approaches (Rao and Tetreault 2018; Li et al. 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), and question answering (Lewis et al. The style of language is crucial because it makes natural language processing more user-centered. 2018; Lample et al. 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. 2016), hand-crafted rules (Khosmood and Levinson 2008; Castro, Ortega, and Muoz 2017), or using hypernyms and definitions to replace the style-carrying words (Karadzhov et al. We can also design criteria that are not computationally easy such as comparing and ranking the outputs of multiple models. Source. Due to previously detected malicious behavior which originated from the network you're using, please request unblock to site. However, prototype editing cannot be applied to all types of style transfer tasks. InfluxDB, This repo collects the articles for text attribute transfer (by zhijing-jin). 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. Abstract. 2017) as in Xu, Ge, and Wei (2019). We define the main notations used in this survey in Table 2. 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. As shown in Fig. Because style transfer data is expensive to annotate, there are not as many parallel datasets as in machine translation. Our curated paper list is at https://github.com/zhijing-jin/Text_Style_Transfer_Survey. 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. Some more arguable ones are male-to-female tone transfer, which can be potentially used for identity deception.
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