Training on Synthetic Noise Improves Robustness to Natural Noise in Machine Translation (D19-55)
Copied to clipboard
| 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. |
Similar Papers
Improving Robustness of Machine Translation with Synthetic Noise (N19-1)
Copied to clipboard
| 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. |
| Outcome: | The proposed method can make a vanilla MT system more resilient to noise, partially mitigating loss in accuracy resulting therefrom. |
Visual Cues and Error Correction for Translation Robustness (2021.findings-emnlp)
Copied to clipboard
| Challenge: | Existing robustness techniques fail when faced with unseen types of noise and their performance degrades on clean texts. |
| Approach: | They propose visual context to improve translation robustness for noisy texts . they also propose an error correction training regime that can be used as an auxiliary task . |
| Outcome: | The proposed training regime improves translation robustness on noisy texts while maintaining translation quality on clean texts. |
Did Translation Models Get More Robust Without Anyone Even Noticing? (2025.acl-long)
Copied to clipboard
| Challenge: | Neural machine translation models are highly sensitive to “noisy” inputs, such as spelling errors, abbreviations, and formatting issues. |
| Approach: | They revisit this insight in light of recent multilingual MT models and large language models applied to machine translation. |
| Outcome: | The proposed models perform better on clean data than previous models, but none of the open models use robustness techniques. |
Machine Translation Robustness to Natural Asemantic Variation (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing machine translation models struggle with noisy data and tail-end words and phrases. |
| Approach: | They introduce and formalize a class of noise and variation that preserves meaning in the target language. |
| Outcome: | The proposed model can perform better on natural asemantic variation (NAV) the proposed model is robust to a variety of perturbations, but not all of them are achieved with organic variations. |
Leveraging Synthetic Targets for Machine Translation (2023.findings-acl)
Copied to clipboard
| Challenge: | Using synthetic target data, training models on synthetic targets outperforms training on actual ground-truth data. |
| Approach: | They propose a recipe for training machine translation models on synthetic target data by leveraging a large pre-trained model. |
| Outcome: | The proposed model outperforms training on real-world translation datasets. |
Beyond Static Synthetic Noise: Assessing the Robustness of Large Language Models to Natural Context Variation in the Real World (2026.findings-acl)
Copied to clipboard
| Challenge: | Current robustness evaluation methods rely on static synthetic perturbations to stress-test models. |
| Approach: | They propose a framework for automatically evaluating QA models under naturally occurring textual perturbations by replacing context passages with revised Wikipedia edit histories. |
| Outcome: | The proposed framework replaces context passages with revised Wikipedia edit histories to improve model performance. |
Beyond Noise: Mitigating the Impact of Fine-grained Semantic Divergences on Neural Machine Translation (2021.acl-long)
Copied to clipboard
| Challenge: | Prior work treats all types of mismatches between source and target as noise . Consequently, it remains unclear how noisy parallel training samples impact NMT training. |
| Approach: | They propose a divergent-aware NMT framework that uses factors to help NMT recover from the degradation caused by naturally occurring divergences. |
| Outcome: | The proposed framework improves translation quality and model calibration on EN-FR tasks. |
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (D19-55)
Copied to clipboard
| Challenge: | Neural Machine Translation models are sensitive to noise in the input data. |
| Approach: | They propose new methods to extend limited noisy data and further improve NMT robustness to noise while keeping the models small. |
| Outcome: | The proposed methods extend limited noisy data and improve robustness to noise while keeping the models small. |
Robust to Noise Models in Natural Language Processing Tasks (P19-2)
Copied to clipboard
| Challenge: | Existing spelling correction systems are far from perfect for noise-sensitive texts . a new way to handle noise is to make models robust to noise. |
| Approach: | They propose a robust to noise word embeddings model which outperforms existing models in different tasks. |
| Outcome: | The proposed model outperforms existing models in three downstream tasks and shows improvements in noise robustness over existing models. |
A Stochastic Decoder for Neural Machine Translation (P18-1)
Copied to clipboard
| Challenge: | Neural machine translation models do not account for local lexical and syntactic variation in parallel corpora. |
| Approach: | They propose a deep generative model of machine translation which incorporates a chain of latent variables to account for local lexical and syntactic variation in parallel corpora. |
| Outcome: | The proposed model consistently improves over strong baselines on several different language pairs. |