| Challenge: | Synthetic translations have been used for a wide range of NLP tasks, but it remains unclear how they differ from naturally occurring data. |
| Approach: | They propose to use a semantic equivalence classifier to improve bitext quality without additional bilingual supervision to replace the originals. |
| Outcome: | The proposed samples improve bitext quality without additional bilingual supervision and are validated intrinsically and extrinsically through bilingual induction and MT tasks. |
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| Challenge: | Existing methods to improve Neural Machine Translation (NMT) for lowresource languages are often trained on heuristically aligned or automatically mined data. |
| Approach: | They propose to filter out imperfect translations that yield unreliable training signals for Neural Machine Translation (NMT) instead, they propose to refine mined bitexts by automatic editing . |
| Outcome: | The proposed method improves the quality of mined bitexts for low-resource languages by up to 8 BLEU points. |
Exploiting Monolingual Data at Scale for Neural Machine Translation (D19-1)
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| Challenge: | Neural machine translation (NMT) is a well-known and expensive task. |
| Approach: | They propose a method to use target-side monolingual data for neural machine translation and propose 'synthetic bitext' they propose generating synthetic bitext by translating monolingual into the other domain using models pretrained on genuine bitext. |
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Understanding Back-Translation at Scale (D18-1)
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| Challenge: | An effective method to improve neural machine translation with monolingual data is to augment the parallel training corpus with back-translations of target language sentences. |
| Approach: | They propose to augment parallel training corpus with back-translations of target language sentences to improve neural machine translation with monolingual data. |
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Improving Robustness of Machine Translation with Synthetic Noise (N19-1)
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| Challenge: | Recent work on MT robustness has demonstrated the need to build or adapt systems that are resilient to such noise. |
| Approach: | They propose to synthesize natural noise in social media data to enhance robustness of MT systems by leveraging natural noise. |
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On Synthetic Data for Back Translation (2022.naacl-main)
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| Challenge: | Existing studies on back translation (BT) focus on beam search or random sampling . a new method to generate synthetic data with a backward model is proposed to improve BT performance. |
| Approach: | They propose a method to generate synthetic data to trade off quality and importance factors . back translation (BT) is one of the most significant technologies in NMT research fields . |
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Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation (D19-55)
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| Challenge: | Recent machine translation methods are highly sensitive to orthographical variations such as spelling errors. |
| Approach: | They propose to train machine translation models with random synthetic noise at training time . they focus on translation performance on natural typos, and show robustness to such noise . |
| Outcome: | The proposed method significantly improves translation models on natural typos without accessing natural noise data or distribution. |
Bilingual Lexicon Induction via Unsupervised Bitext Construction and Word Alignment (2021.acl-long)
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| Challenge: | Existing methods for bilingual lexicon induction are linear and require simplifying assumptions. |
| Approach: | They propose methods that combine unsupervised bitext mining and unsupervised word alignment to produce higher quality lexicons. |
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Refer to the Reference: Reference-focused Synthetic Automatic Post-Editing Data Generation (2025.coling-main)
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| Challenge: | Existing approaches to synthetic APE data generation use source (src) sentences in a parallel corpus to obtain translations (mt) through an MT system and treat corresponding reference (ref) sentences as post-edits (pe). |
| Approach: | They propose a reference-focused synthetic APE data generation technique that uses ‘ref’ instead of src’ sentences to obtain corrupted translations. |
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Synthetic Pre-Training Tasks for Neural Machine Translation (2023.findings-acl)
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| Challenge: | toxicity and bias can be addressed by pre-training with synthetic resources . BLEU scores are used to compare methods with real-world data . |
| Approach: | They propose several ways to generate obfuscated data from large parallel corpus and concatenating phrase pairs from small word-aligned corpus with synthetic parallel data without real human language corpora. |
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AugVic: Exploiting BiText Vicinity for Low-Resource NMT (2021.findings-acl)
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| Challenge: | Neural Machine Translation (NMT) systems often exhibit poor performance due to the lack of large bitext training corpora in low-resource languages. |
| Approach: | They propose a data augmentation framework which exploits the vicinal samples of the given bitext without using extra monolingual data explicitly. |
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