Who cites «Reformulating Unsupervised Style Transfer as Paraphrase Generation» · page 2
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- SimpleStyle: An Adaptable Style Transfer Approach — 2022 · arXiv:2212.10498
- So Different Yet So Alike! Constrained Unsupervised Text Style Transfer — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- Style Transfer as Data Augmentation: A Case Study on Named Entity Recognition — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- StyleKQC: A Style-Variant Paraphrase Corpus for Korean Questions and Commands — 2022 · Proceedings of the Thirteenth Language Resources and Evaluation Conference
- T-STAR: Truthful Style Transfer using AMR Graph as Intermediate Representation — our card · 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- Text Style Transfer via Optimal Transport — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Towards Robust and Semantically Organised Latent Representations for Unsupervised Text Style Transfer — 2022 · Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- UCTopic: Unsupervised Contrastive Learning for Phrase Representations and Topic Mining — 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- VALUE: Understanding Dialect Disparity in NLU — our card · 2022 · Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)
- arXivEdits: Understanding the Human Revision Process in Scientific Writing — 2022 · Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing
- A Recipe For Arbitrary Text Style Transfer with Large Language Models — 2021 · arXiv:2109.03910
- Beyond Grammatical Error Correction: Improving L1-influenced research writing in English using pre-trained encoder-decoder models — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
- Compression, Transduction, and Creation: A Unified Framework for Evaluating Natural Language Generation — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Controlling Machine Translation for Multiple Attributes with Additive Interventions — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- ConvFiT: Conversational Fine-Tuning of Pretrained Language Models — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Does It Capture STEL? A Modular, Similarity-based Linguistic Style Evaluation Framework — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Don''t Go Far Off: An Empirical Study on Neural Poetry Translation — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Evaluating the Evaluation Metrics for Style Transfer: A Case Study in Multilingual Formality Transfer — our card · 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Fundamental Exploration of Evaluation Metrics for Persona Characteristics of Text Utterances — 2021 · Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue
- IGA: An Intent-Guided Authoring Assistant — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Idiomatic Expression Paraphrasing without Strong Supervision — 2021 · arXiv:2112.08592
- LEWIS: Levenshtein Editing for Unsupervised Text Style Transfer — 2021 · Findings of the Association for Computational Linguistics: ACL-IJCNLP 2021
- Methods for Detoxification of Texts for the Russian Language — 2021 · arXiv:2105.09052
- Mind the Style of Text! Adversarial and Backdoor Attacks Based on Text Style Transfer — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Mitigating Data Scarceness through Data Synthesis, Augmentation and Curriculum for Abstractive Summarization — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
- Multi-granularity Textual Adversarial Attack with Behavior Cloning — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Olá, Bonjour, Salve! XFORMAL: A Benchmark for Multilingual Formality Style Transfer — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Phrase-BERT: Improved Phrase Embeddings from BERT with an Application to Corpus Exploration — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Rethinking Sentiment Style Transfer — 2021 · Findings of the Association for Computational Linguistics: EMNLP 2021
- StylePTB: A Compositional Benchmark for Fine-grained Controllable Text Style Transfer — 2021 · Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies
- Text Detoxification using Large Pre-trained Neural Models — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- TextSETTR: Few-Shot Text Style Extraction and Tunable Targeted Restyling — 2021 · Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
- The Perils of Using Mechanical Turk to Evaluate Open-Ended Text Generation — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- Transductive Learning for Unsupervised Text Style Transfer — 2021 · Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
- XFORMAL: A Benchmark for Multilingual Formality Style Transfer — 2021 · arXiv:2104.04108
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