| Challenge: | We show a 5.8 point increase in BLEU on heavily code-mixed sentences . code-mixing is becoming more commonplace in several bilingual communities . |
| Approach: | They propose a method to convert existing parallel data sources into code-mixed parallel data. |
| Outcome: | The proposed method shows a 5.8 point increase in BLEU on heavily code-mixed sentences on a Hindi-English code-mixed translation task. |
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| Challenge: | Prior work has addressed the lack of gold standard code-mixed to pure language parallel data with data augmentation techniques. |
| Approach: | They propose a back-translation-based training scheme for code-mixed translation which eliminates dependence on external resources. |
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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. |
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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. |
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Target-Side Augmentation for Document-Level Machine Translation (2023.acl-long)
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Data Augmentation for Code Translation with Comparable Corpora and Multiple References (2023.findings-emnlp)
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| Challenge: | Existing methods for translating code between programming languages are limited by parallel training data. |
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Counterfactual Data Augmentation for Neural Machine Translation (2021.naacl-main)
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| Challenge: | Neural machine translation models often rely on large-scale parallel corpora for training, exhibiting degraded performance on low-resource languages. |
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A Semi-supervised Approach to Generate the Code-Mixed Text using Pre-trained Encoder and Transfer Learning (2020.findings-emnlp)
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| Challenge: | Existing methods to train neural network-based models for code-mixing are limited due to language specificity of code-mixed text. |
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Sequence-Level Mixed Sample Data Augmentation (2020.emnlp-main)
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| Challenge: | Despite their empirical success, neural networks still have difficulty capturing compositional aspects of natural language. |
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Translate & Fill: Improving Zero-Shot Multilingual Semantic Parsing with Synthetic Data (2021.findings-emnlp)
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| Challenge: | Existing methods to improve semantic parsing performance on target languages are limited. |
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Few-shot learning through contextual data augmentation (2021.eacl-main)
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| Challenge: | Various strategies have been explored to learn from a journalist's post-edits . state-of-the-art APE systems require large numbers of post- edits for training . |
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