| Challenge: | Existing methods for enhancing training data are limited in natural language tasks due to text characteristics. |
| Approach: | They propose a data augmentation method that softly augments a randomly chosen word in a sentence by its contextual mixture of multiple related words. |
| Outcome: | The proposed method outperforms baseline methods on small and large scale machine translation datasets. |
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Unifying Input and Output Smoothing in Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent methods that smooth input and output of neural machine translation systems bring significant improvements in performance. |
| Approach: | They propose a method that replaces one-hot representations with soft posterior distributions of an external language model, smoothing the input of machine translation systems. |
| Outcome: | The proposed method improves translation performance on small datasets and larger datasets. |
Boosting Neural Machine Translation with Similar Translations (2020.acl-main)
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| Challenge: | Statistical Machine Translation and fuzzy matching are completely different in their finality. |
| Approach: | They propose to use fuzzy matching to train neural machine translation to make use of similar translations, in a similar way a human translator employs fuzzy matches. |
| Outcome: | The proposed methods improve translation accuracy and fine-tuned model for unseen translation pairs. |
Contextual Augmentation: Data Augmentation by Words with Paradigmatic Relations (N18-2)
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| Challenge: | Neural network-based models for NLP have been growing with state-of-the-art results in various tasks. |
| Approach: | They propose a data augmentation method for labeled sentences called contextual augmentation. |
| Outcome: | The proposed method improves classifiers based on convolutional or recurrent neural networks. |
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. |
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. |
| Approach: | They propose a method that interprets language models and phrasal alignment causally and generates augmented parallel translation corpora by sampling new source phrases from a masked language model. |
| Outcome: | The proposed method improves translation, backtranslation and translation robustness on IWSLT’15 English Vietnamese, WMT’17 English - German, and WMT'18 English – Turkish. |
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. |
SwitchOut: an Efficient Data Augmentation Algorithm for Neural Machine Translation (D18-1)
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| Challenge: | Existing methods for data augmentation for text-based tasks such as machine translation are limited due to noise and noise. |
| Approach: | They propose a data augmentation policy with desirable properties as an optimization problem and propose 'SwitchOut' switchout randomly replaces words in both the source and target sentences with other random words from their corresponding vocabularies. |
| Outcome: | The proposed method outperforms strong alternatives such as word dropout on three translation datasets. |
Deterministic Reversible Data Augmentation for Neural Machine Translation (2024.findings-acl)
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| Challenge: | Recent neural machine translation models have improved translation quality but they also introduce small perturbations like misspelling and paraphrasing. |
| Approach: | They propose a method that generates multi-granularity subword representations with reversible operations and deterministic segmentations. |
| Outcome: | The proposed method outperforms strong baselines on several translation tasks with a clear margin and exhibits good robustness in noisy, low-resource, and cross-domain datasets. |
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 . |
| Approach: | They propose to teach a pre-trained machine translation model to translate previously unseen words accurately . they extend a data augmentation approach to create training examples with similar contexts . |
| Outcome: | The proposed model improves accuracy on the scale of one to five examples with only 1 to 5 examples. |
Improving Lexical Choice in Neural Machine Translation (N18-1)
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| Challenge: | False positives: the output layer rewards frequent words disproportionately, we argue . Falsibles: a model that learns word representations in continuous space tends to translate rare words . |
| Approach: | They propose to fix the norms of both vectors to a constant value and integrate a lexical module which is jointly trained with the rest of the model. |
| Outcome: | The proposed approach achieves improvements of up to +4.3 BLEU surpassing phrase-based translation in nearly all settings. |