| Challenge: | Low-resource language pairs with a lack of parallel data pose challenges for machine translation . data augmentation using monolingual data is an effective way to alleviate the problem . |
| Approach: | They propose a general framework for data augmentation for low-resource machine translation using monolingual data and a related high-resourced language. |
| Outcome: | The proposed method improves translation quality by 1.5 to 8 BLEU points under extreme low-resource settings compared to baselines. |
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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. |
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A systematic comparison of methods for low-resource dependency parsing on genuinely low-resource languages (D19-1)
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| Challenge: | Large annotated treebanks are available for only a tiny fraction of the world's languages, and there is a wealth of literature on strategies for parsing with few resources. |
| Approach: | They propose three strategies for improving low-resource parsers: data augmentation, cross-lingual training, and transliteration. |
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Language Model Priors and Data Augmentation Strategies for Low-resource Machine Translation: A Case Study Using Finnish to Northern Sámi (2024.findings-acl)
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| Challenge: | a new study examines the use of monolingual data for improving low-resource machine translation. |
| Approach: | They investigate ways of using monolingual data for improving low-resource machine translation. |
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Small Data, Big Impact: Leveraging Minimal Data for Effective Machine Translation (2023.acl-long)
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Jean Maillard, Cynthia Gao, Elahe Kalbassi, Kaushik Ram Sadagopan, Vedanuj Goswami, Philipp Koehn, Angela Fan, Francisco Guzman
| Challenge: | Existing datasets are not economical to create large-scale datasets, but for low-resource languages, a few thousand professionally translated sentence pairs can be useful. |
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Data Augmentation via Subtree Swapping for Dependency Parsing of Low-Resource Languages (2020.coling-main)
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| Challenge: | Lack of annotated training data is a big issue for building reliable NLP systems for most of the world’s languages. |
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Grammar-based Data Augmentation for Low-Resource Languages: The Case of Guarani-Spanish Neural Machine Translation (2024.naacl-long)
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Agustín Lucas, Alexis Baladón, Victoria Pardiñas, Marvin Agüero-Torales, Santiago Góngora, Luis Chiruzzo
| Challenge: | Low-resource languages suffer from a vicious circle: data is needed to build tools, but available text is scarce. |
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Few-Shot Learning Translation from New Languages (2025.emnlp-main)
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| Challenge: | Recent work shows strong transfer learning capability to unseen languages in sequence-to-sequence neural networks . current transfer learning methods require much less downstream task data than would otherwise be required. |
| Approach: | They first train word embeddings models on varying amounts of data and plug them into a machine translation model. |
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Adapting High-resource NMT Models to Translate Low-resource Related Languages without Parallel Data (2021.acl-long)
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Wei-Jen Ko, Ahmed El-Kishky, Adithya Renduchintala, Vishrav Chaudhary, Naman Goyal, Francisco Guzmán, Pascale Fung, Philipp Koehn, Mona Diab
| Challenge: | linguistic overlap between low-resource languages and high-resourced languages is a major obstacle for training high-quality machine translation systems. |
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Handling Syntactic Divergence in Low-resource Machine Translation (D19-1)
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| Challenge: | Existing approaches to neural machine translation (NMT) are dependent on limited parallel data, and can be difficult to use for many language pairs. |
| Approach: | They propose a method where target-language sentences are re-ordered to match the order of the source and used as an additional source of training-time supervision. |
| Outcome: | The proposed method improves on simulated low-resource Japanese-to-English and real low-demand Uyghur-to English scenarios. |
Getting More Data for Low-resource Morphological Inflection: Language Models and Data Augmentation (2020.lrec-1)
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| Challenge: | Morphological inflection is the process that generates the word form given its lexeme and morphological properties. |
| Approach: | They propose to use language models and data augmentation to improve morphological inflection without annotating more data. |
| Outcome: | The proposed model improves by 1.5% with the langauge model and by 9% with the data augmentation. |