Disentangled Representation Learning for Non-Parallel Text Style Transfer (P19-1)
Copied to clipboard
| Challenge: | a paper aims to disentangle latent representations of style and content in language models . auxiliary multi-task and adversarial objectives are used to disentangle the latent space . |
| Approach: | They propose a simple yet effective approach to disentangling latent representations . they propose auxiliary multi-task and adversarial objectives to disentangle style and content . |
| Outcome: | The proposed approach achieves high performance in terms of transfer accuracy, content preservation, and language fluency compared to previous approaches . |
Similar Papers
An Evaluation of Disentangled Representation Learning for Texts (2021.findings-acl)
Copied to clipboard
| Challenge: | Disentangled representations of texts encode information pertaining to different aspects of the text in separate vector embeddings. |
| Approach: | They propose to use a highly-structured natural language dataset to evaluate disentangled representations for texts. |
| Outcome: | The proposed models are well-suited for learning disentangled representations of texts on a synthetic natural language dataset. |
Style Transformer: Unpaired Text Style Transfer without Disentangled Latent Representation (P19-1)
Copied to clipboard
| Challenge: | Disentangling the content and style in the latent space is prevalent in text style transfer . recurrent neural networks (RNN) based encoder and decoder cannot deal with the long-term dependency . |
| Approach: | They propose a style transformer which disentangles style information in latent space . they propose encoding and decoding methods that disentangle style information . |
| Outcome: | The proposed method can achieve better style transfer and better content preservation. |
Counterfactuals to Control Latent Disentangled Text Representations for Style Transfer (2021.acl-short)
Copied to clipboard
| Challenge: | Existing methods for unsupervised text style transfer focus on transferring a specific attribute, but this technique has never been explored in natural language generation tasks. |
| Approach: | They propose a counterfactual-based method to modify latent representations by posing a ‘what-if’ scenario. |
| Outcome: | The proposed method is tested on multiple attribute transfer tasks like Sentiment, Formality and Excitement to support the hypothesis. |
Semi-supervised Text Style Transfer: Cross Projection in Latent Space (D19-1)
Copied to clipboard
| Challenge: | Text style transfer task has long suffered from the shortage of parallel data . |
| Approach: | They propose a semi-supervised text style transfer model that combines parallel data with large-scale nonparallel data to train it. |
| Outcome: | The proposed model can transfer a sentence of one style to another while retaining its original content meaning while preserving its original meaning. |
Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer (2022.naacl-main)
Copied to clipboard
| Challenge: | Recent studies show that auto-encoders perform language generation, smooth sentence interpolation, and style transfer over unseen attributes using unlabelled datasets in a zero-shot manner. |
| Approach: | They propose a discrete token-based perturbation approach to map "similar" sentences close by in latent space. |
| Outcome: | The proposed model can generate and perform language generation, style transfer and sentence interpolation tasks on unlabelled datasets in a zero-shot manner. |
Can Authorship Representation Learning Capture Stylistic Features? (2023.tacl-1)
Copied to clipboard
Andrew Wang, Cristina Aggazzotti, Rebecca Kotula, Rafael Rivera Soto, Marcus Bishop, Nicholas Andrews
| Challenge: | Existing methods to disentangle an author's style from the content of their writing are limited by the reliance on human labels and the narrow focus of stylistic distinctions. |
| Approach: | They propose to use a surrogate task to learn authorship representations that are sensitive to writing style and to validate their hypothesis . |
| Outcome: | The proposed representations are sensitive to writing style and may be robust to topic drift over time. |
Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora (D19-55)
Copied to clipboard
| Challenge: | Typical datasets used for style transfer in NLP contain aligned pairs of two opposite extremes of a style. |
| Approach: | They propose a technique to derive a dataset of aligned pairs from an unlabeled corpus by using an auxiliary dataset, allowing for in-domain training. |
| Outcome: | The proposed method significantly outperforms OpenNMT’s Seq2Seq model trained on the Yahoo Formality Dataset and 6 novel datasets. |
StyleFlow: Disentangle Latent Representations via Normalizing Flow for Unsupervised Text Style Transfer (2024.lrec-main)
Copied to clipboard
| Challenge: | Existing methods to separate content from style but some words contain both content and style information. |
| Approach: | They propose a method which uses a reversible encoder to improve content disentanglement. |
| Outcome: | The proposed method outperforms baselines on sentiment transfer and formality transfer tasks. |
Learning Disentangled Representations of Negation and Uncertainty (2022.acl-long)
Copied to clipboard
| Challenge: | Negation and uncertainty modeling are long-standing tasks in natural language processing. |
| Approach: | They propose to disentangle negation, uncertainty, and content using a Variational Autoencoder by supervising latent representations using auxiliary objectives. |
| Outcome: | The proposed model can disentangle negation, uncertainty, and content using a Variational Autoencoder. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
Copied to clipboard
Pawel Bujnowski, Kseniia Ryzhova, Hyungtak Choi, Katarzyna Witkowska, Jaroslaw Piersa, Tymoteusz Krumholc, Katarzyna Beksa
| Challenge: | a limited amount of style data is needed for text style transfer, but there are no convincing methods for evaluating them. |
| Approach: | They propose an efficient method for neutral-to-style transformation using the transformer framework. |
| Outcome: | The proposed method can train neutral-to-style transformation models using large paraphrases and a small style transfer corpus. |