| 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. |
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Neural Fuzzy Repair: Integrating Fuzzy Matches into Neural Machine Translation (P19-1)
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| Challenge: | Several configurations are tested on the DGT-TM data set for the language directions English into Dutch (ENNL) and English into Hungarian (ENHU). |
| Approach: | They propose and test two methods for augmenting NMT training data with fuzzy TM matches by concatenation. |
| Outcome: | The proposed method improves on the DGT-TM data set for two language pairs and is easy to implement. |
Soft Contextual Data Augmentation for Neural Machine Translation (P19-1)
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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 Modeling the Style of Translators in Neural Machine Translation (2021.naacl-main)
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| Challenge: | a key ingredient of neural machine translation is the use of large datasets with different but consistent translation styles . however, the models do not capture the variety of translators' styles from the data . a recent study shows that style-augmented models can capture the style variations of translator . |
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Improving Retrieval Augmented Neural Machine Translation by Controlling Source and Fuzzy-Match Interactions (2023.findings-eacl)
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| Challenge: | a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. |
| Approach: | They propose a zero-shot adaptation approach where a general-domain model has access to customer or domain specific parallel data at inference time, but not during training. |
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Enhancing Neural Machine Translation Through Target Language Data: A kNN-LM Approach for Domain Adaptation (2025.acl-long)
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Abudurexiti Reheman, Hongyu Liu, Junhao Ruan, Abudukeyumu Abudula, Yingfeng Luo, Tong Xiao, JingBo Zhu
| Challenge: | Neural machine translation (NMT) has made significant progress in recent years, yet often suffers from translating in new domains, which is called domain adaptation. |
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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. |
| Approach: | They propose to evaluate DA methods from two perspectives to determine their generalization ability . they find that DA method's test performance does not exhibit consistent improvements across translation tasks . |
| Outcome: | The proposed methods do not exhibit consistent improvements across translation tasks. |
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. |
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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. |
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Improving Multilingual Neural Machine Translation by Utilizing Semantic and Linguistic Features (2024.findings-acl)
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| Challenge: | Existing models do not differentiate between semantic and linguistic features, resulting in the entanglement of knowledge and linguistics within the model. |
| Approach: | They propose to exploit both semantic and linguistic features to enhance multilingual translation by disentangling encoder representations and integrating low-level linguistic encoders. |
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Improving Language Model Integration for Neural Machine Translation (2023.findings-acl)
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| Challenge: | Existing methods to integrate external language models into machine translation systems have been based on the assumption that the external model learns an implicit target-side language model at decoding time. |
| Approach: | They transfer this concept to the task of machine translation and compare it with the most prominent way of including additional monolingual data - namely back-translation. |
| Outcome: | The proposed approach outperforms the most prominent way of including additional monolingual data, namely back-translation. |