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. |
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| 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. |
Towards a Better Understanding of Label Smoothing in Neural Machine Translation (2020.aacl-main)
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| Challenge: | In recent years, Neural Network (NN) models bring steady and concrete improvements on the task of Machine Translation (MT). |
| Approach: | They propose to penalize over-confident outputs and regularize the model so that its outputs do not diverge too much from some prior distribution. |
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Text Smoothing: Enhance Various Data Augmentation Methods on Text Classification Tasks (2022.acl-short)
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| Challenge: | Experimental results show text smoothing outperforms data augmentation methods by a substantial margin. |
| Approach: | They propose to use a masked language model to convert a token to a smoothed representation by converting a sentence from its one-hot representation to 'controllable smoothes' they propose to combine text smoothing with other data augmentation methods to achieve better performance. |
| Outcome: | The proposed method outperforms mainstream data augmentation methods by a substantial margin on different datasets in a low-resource regime. |
Understanding Data Augmentation in Neural Machine Translation: Two Perspectives towards Generalization (D19-1)
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| Challenge: | Existing studies measure the superiority of DA methods in terms of their performance on a specific test set, but some do not exhibit consistent improvements across translation tasks. |
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| Outcome: | The proposed methods do not exhibit consistent improvements across translation tasks. |
The Role of n-gram Smoothing in the Age of Neural Networks (2024.naacl-long)
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| Challenge: | n-gram smoothing techniques were used to overcome overfitting problems in neural language models for decades. |
| Approach: | They propose to convert any n-gram smoothing technique into a regularizer compatible with neural language models. |
| Outcome: | The proposed regularizers outperform label smoothing on language modeling and machine translation. |
Focus on the Target’s Vocabulary: Masked Label Smoothing for Machine Translation (2022.acl-short)
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| Challenge: | Label smoothing and vocabulary sharing are widely used in neural machine translation models, but they can be conflicting and lead to suboptimal performance. |
| Approach: | They propose a mechanism that masks the soft label probability of source-side words to zero and integrates label smoothing with vocabulary sharing to improve translation quality. |
| Outcome: | The proposed mechanism improves translation quality and model calibration on bilingual and multilingual datasets, while retaining the original smoothing method. |
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. |
Enhancing Language Model Alignment: A Confidence-Based Approach to Label Smoothing (2024.emnlp-main)
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| Challenge: | Large Language Models (LLMs) have remarkable capabilities across various domains . Reinforcement Learning with Human Feedback (RLHF) phase is crucial for training . label smoothing is a technique that replaces hard labels with soft labels . |
| Approach: | They propose a method that iteratively updates the label smoothing parameter based on preference labels and model forecasts. |
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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. |
Improving Neural Machine Translation Robustness via Data Augmentation: Beyond Back-Translation (D19-55)
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| 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. |