Papers with Neural Machine Translation models
Visual Cues and Error Correction for Translation Robustness (2021.findings-emnlp)
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| 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. |
Explaining How Transformers Use Context to Build Predictions (2023.acl-long)
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| Challenge: | Existing methods for analyzing input attributions for a model's prediction are unclear how prior words affect the model' s decision throughout the layers. |
| Approach: | They propose a procedure to analyze models for language generation using the Transformer and a comparison of their results with evidence of the linguistic phenomena. |
| Outcome: | The proposed method consistently aligns better than gradient-based and perturbation-based baselines and generates human-like source-target alignments for building predictions. |
Critical Learning Periods: Leveraging Early Training Dynamics for Efficient Data Pruning (2024.findings-acl)
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| Challenge: | Neural Machine Translation models are extremely data-hungry and require a large dataset to maintain data quality. |
| Approach: | They propose a new data pruning technique that leverages early model training dynamics to identify the most relevant data points for model performance. |
| Outcome: | The proposed technique outperforms the benchmarks on indo-European languages while pruning up to 50% of training data. |
Does Context Help Mitigate Gender Bias in Neural Machine Translation? (2024.findings-emnlp)
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| Challenge: | Neural machine translation models perpetuate gender bias in their training data distribution. |
| Approach: | They examine the gender bias in Neural Machine Translation by using context-aware models to enhance translation accuracy for feminine terms and translation with non-informative context in Basque to Spanish. |
| Outcome: | The proposed models can maintain or even amplify gender bias in translations of stereotypical professions in English and with non-informative context in Basque to Spanish. |