| Challenge: | Formality style transfer is a task of automatically transforming text in one particular formality style into another. |
| Approach: | They propose to augment parallel data with three specific data augmentation methods to improve the model's generalization ability and reduce the overfitting risk. |
| Outcome: | The proposed methods significantly improve performance when used to pre-train the model and lead to the state-of-the-art results in the GYAFC benchmark dataset. |
Similar Papers
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. |
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 . |
Exploring Data Augmentation for Code Generation Tasks (2023.findings-eacl)
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| Challenge: | Recent advances in natural language processing have impacted how models are trained for programming language tasks. |
| Approach: | They propose to use augmentation methods that yield consistent improvements in code translation and summarization by up to 6.9% and 7.5% respectively. |
| Outcome: | The proposed methods improve translation and summarization by 6.9% and 7.5% respectively. |
Generic resources are what you need: Style transfer tasks without task-specific parallel training data (2021.emnlp-main)
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| Challenge: | Text style transfer is a task aimed at converting a text of one style into another while preserving its content. |
| Approach: | They propose a multi-step procedure which builds on a generic pre-trained sequence-to-sequence model and an iterative back-translation approach to train two models in a transfer direction. |
| Outcome: | The proposed method outperforms existing unsupervised approaches on the two most popular style transfer tasks: formality transfer and polarity swap. |
Rethinking Data Augmentation for Low-Resource Neural Machine Translation: A Multi-Task Learning Approach (2021.emnlp-main)
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| Challenge: | Existing approaches to generating additional parallel sentences are aimed at expanding the support of the empirical data distribution by generating new sentence pairs that contain infrequent words. |
| Approach: | They propose to use data augmentation techniques to generate additional parallel sentences by reversing the order of the target sentence to produce unfluent target sentences. |
| Outcome: | The proposed approach improves on six low-resource translation tasks and the baseline and over DA methods. |
Sentence Concatenation Approach to Data Augmentation for Neural Machine Translation (2021.naacl-srw)
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| Challenge: | Neural machine translation is known to show poor performance at long sentence translations . however, when the sentence length exceeds a certain value, the quality of NMT becomes inferior to that of statistical machine translation. |
| Approach: | They propose a method that uses given parallel corpora as train data to generate long sentences by concatenating two sentences at random. |
| Outcome: | The proposed method improves translation quality more when combined with back-translation. |
A Survey of Data Augmentation Approaches for NLP (2021.findings-acl)
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Steven Y. Feng, Varun Gangal, Jason Wei, Sarath Chandar, Soroush Vosoughi, Teruko Mitamura, Eduard Hovy
| Challenge: | Data augmentation is a field of research that has been underexplored due to the discrete nature of language data. |
| Approach: | They present a comprehensive survey of data augmentation for NLP by summarizing the literature in a structured manner. |
| Outcome: | The proposed methods are used for popular NLP applications and tasks and highlight current challenges and directions for future research. |
On the Role of Parallel Data in Cross-lingual Transfer Learning (2023.findings-acl)
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| Challenge: | Existing multilingual models do not exploit the full potential of monolingual data, a new study finds . prior work has shown that parallel data is beneficial for cross-lingual learning, but it is unclear if it is the data itself or the modeling of parallel interactions that matters. |
| Approach: | They compare unsupervised machine translation to supervised machine translator and gold parallel data to generate synthetic parallel data. |
| Outcome: | The proposed model generated parallel data is better than supervised machine translation and gold parallel data in both general and task-specific settings. |
Thank you BART! Rewarding Pre-Trained Models Improves Formality Style Transfer (2021.acl-short)
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| Challenge: | Formality style transfer models have limited success in preserving content due to the scarcity of parallel data. |
| Approach: | They propose to fine-tune pre-trained language and sequence-to-sequence models with rewards that target style and content to enhance content preservation. |
| Outcome: | The proposed models can be fine-tuned with rewards that target style and content, and achieve good performance even with limited amounts of parallel data. |
Simple and effective data augmentation for compositional generalization (2024.naacl-long)
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| Challenge: | Compositional generalization is the ability of a system to correctly predict the meaning of complex sentences when trained on simpler sentences. |
| Approach: | They propose to use data augmentation methods to generate additional training data by sampling from an augmentation distribution to generalize to the out-of-distribution test data. |
| Outcome: | The proposed method outperforms existing methods that sampled from the training distribution and outperformed existing methods. |