Dynamic Data Selection and Weighting for Iterative Back-Translation (2020.emnlp-main)
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| Challenge: | Experimental results demonstrate that our methods achieve improvements of up to 1.8 BLEU points over competitive baselines. |
| Approach: | They propose a data selection and weighting strategy to iterate back-translation models and apply it to it . they use a target language to back-transcribe monolingual data, which is of high quality and reflect the target domain. |
| Outcome: | The proposed approach achieves 1.8 BLEU points over baselines on domain adaptation, low-resource, and high-resourced MT settings and on two language pairs. |
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| Challenge: | incorporating backtranslated data from different sources has led to improved results in machine translation (MT) |
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| Challenge: | Neural machine translation (NMT) uses a sequence-to-sequence model to generate synthetic data. |
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| Challenge: | Existing studies show that training on a single related language is more effective than using all data. |
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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. |
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Data Selection Curriculum for Neural Machine Translation (2022.findings-emnlp)
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| Challenge: | Neural Machine Translation models are typically trained on heterogeneous data that are concatenated and randomly shuffled. |
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Iterative Domain-Repaired Back-Translation (2020.emnlp-main)
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| Challenge: | Existing studies show that NMT models perform poorly in specific domains when in-domain parallel corpora are scarce or nonexistent. |
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Reinforcement Learning based Curriculum Optimization for Neural Machine Translation (N19-1)
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| Challenge: | a heterogeneous training dataset can vary in characteristics such as domain, translation quality, and degree of difficulty. |
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Adaptive Weighting for Neural Machine Translation (C18-1)
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| Challenge: | Existing weighted sum models (WSMs) take inputs and generate one output, but they are independent of each other and are fixed for all inputs. |
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Dynamic Curriculum Learning for Low-Resource Neural Machine Translation (2020.coling-main)
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| Challenge: | Recent work on neural machine translation (NMT) has demonstrated impressive performance improvements and became the de-facto standard. |
| Approach: | They propose a dynamic curriculum learning method to reorder training samples in training using a Transformer-based system. |
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A Semantic Uncertainty Sampling Strategy for Back-Translation in Low-Resources Neural Machine Translation (2025.acl-srw)
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Yepai Jia, Yatu Ji, Xiang Xue, Shilei@imufe.edu.cn Shilei@imufe.edu.cn, Qing-Dao-Er-Ji Ren, Nier Wu, Na Liu, Chen Zhao, Fu Liu
| Challenge: | Back-translation methods rely on large-scale parallel corpora to enhance performance, but ignore the semantic quality of monolingual data. |
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