Aman Madaan, Amrith Setlur, Tanmay Parekh, Barnabas Poczos, Graham Neubig, Yiming Yang, Ruslan Salakhutdinov, Alan W Black, Shrimai Prabhumoye
| Challenge: | Prior work on text style transfer has not focused on politeness as a style transfer task and we argue that defining it is cumbersome. |
| Approach: | They propose a task of politeness transfer which involves converting non-polite sentences to polite sentences while preserving the meaning. |
| Outcome: | The proposed model outperforms state-of-the-art methods on content preservation and style transfer accuracy. |
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Polite Chatbot: A Text Style Transfer Application (2023.eacl-srw)
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| Challenge: | Creating polite chatbots requires complex setups that require reinforcement learning to produce coherent responses. |
| Approach: | They propose a polite chatbot that can generate coherent responses to given contexts by using a model that transfers neutral sentences into polite ones and trains a dialogue model. |
| Outcome: | The proposed method outperforms baselines in producing polite dialogue responses while staying competitive in terms of coherent to the given context. |
Delete, Retrieve, Generate: a Simple Approach to Sentiment and Style Transfer (N18-1)
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| Challenge: | Previous work using adversarial methods has struggled to produce high-quality outputs. |
| Approach: | They propose a method that transforms a sentence to alter a specific attribute while preserving its attribute-independent content. |
| Outcome: | The proposed method generates grammatical and appropriate responses on 22% more inputs than the best previous system, averaged over three attribute transfer datasets. |
Formality Style Transfer for Noisy, User-generated Conversations: Extracting Labeled, Parallel Data from Unlabeled Corpora (D19-55)
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| 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. |
Towards Actual (Not Operational) Textual Style Transfer Auto-Evaluation (D19-55)
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| Challenge: | elucidates the dangerous current state of style transfer auto-evaluation research. |
| Approach: | They propose ways to aggregate the three metrics into one evaluator. |
| Outcome: | The proposed method could be used to aggregate the three metrics into one evaluator. |
Unsupervised Evaluation Metrics and Learning Criteria for Non-Parallel Textual Transfer (D19-56)
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| Challenge: | Existing methods for textual transfer with no parallel corpora are insufficient to evaluate textual paraphrases with modified attributes or properties. |
| Approach: | They propose to add a metric for post-transfer classification accuracy and a method to combine them into a single overall score. |
| Outcome: | The proposed metrics correlate well with human judgments, at both the sentence-level and system-level. |
Reformulating Unsupervised Style Transfer as Paraphrase Generation (2020.emnlp-main)
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| Challenge: | Existing systems for style transfer warp the input’s meaning through attribute transfer, which changes semantic properties such as sentiment. |
| Approach: | They propose a method for fine-tuning pretrained language models on automatically generated paraphrase data to improve the efficiency of style transfer. |
| Outcome: | The proposed method outperforms state-of-the-art style transfer systems on human and automatic evaluations and proposes fixed variants. |
Dear Sir or Madam, May I Introduce the GYAFC Dataset: Corpus, Benchmarks and Metrics for Formality Style Transfer (N18-1)
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| Challenge: | a lack of training and evaluation datasets, benchmarks and automatic metrics has blocked progress in this field. |
| Approach: | They propose to use a grammarly's Yahoo Answers Formality corpus to create the largest corpus for a particular style . they also propose to apply machine translation metrics to the task . |
| Outcome: | The proposed model can be used to train and evaluate a text in a particular style . the proposed model is based on the existing model and can be applied to other tasks . |
On the Way to Gentle AI Counselor: Politeness Cause Elicitation and Intensity Tagging in Code-mixed Hinglish Conversations for Social Good (2024.findings-naacl)
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| Challenge: | Politeness is a multifaceted concept influenced by individual perceptions of what is considered polite or impolite. |
| Approach: | They propose a task to identify the underlying reasons behind the use of politeness and gauge the degree of politity conveyed. |
| Outcome: | The proposed method is compared against state-of-the-art datasets and their results show it is superior. |
Towards Content Transfer through Grounded Text Generation (N19-1)
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| Challenge: | Recent work in neural natural language generation has attracted significant interest in controlling the form of text, such as style, persona, and wordiness. |
| Approach: | They propose a task where the task is to generate a next sentence in a document that fits its context and is grounded in . external textual source such as a news story. |
| Outcome: | The proposed task is based on 640k Wikipedia referenced sentences paired with the source articles to show significant improvements against baselines. |
An Empirical Study on Multi-Task Learning for Text Style Transfer and Paraphrase Generation (2020.coling-industry)
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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. |