Papers › Data Diversification: A Simple Strategy For Neural Machine Translation

Data Diversification: A Simple Strategy For Neural Machine Translation

5 Nov 2019NeurIPS 2020 12arXiv:1911.01986archive 2025-07-28

Xuan-Phi Nguyen, Shafiq Joty, Wu Kui, Ai Ti Aw

We introduce Data Diversification: a simple but effective strategy to boost neural machine translation (NMT) performance. It diversifies the training data by using the predictions of multiple forward and backward models and then merging them with the original dataset on which the final NMT model is trained. Our method is applicable to all NMT models. It does not require extra monolingual data like back-translation, nor does it add more computations and parameters like ensembles of models. Our method achieves state-of-the-art BLEU scores of 30.7 and 43.7 in the WMT'14 English-German and English-French translation tasks, respectively. It also substantially improves on 8 other translation tasks: 4 IWSLT tasks (English-German and English-French) and 4 low-resource translation tasks (English-Nepali and English-Sinhala). We demonstrate that our method is more effective than knowledge distillation and dual learning, it exhibits strong correlation with ensembles of models, and it trades perplexity off for better BLEU score. We have released our source code at https://github.com/nxphi47/data_diversification

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Tasks

Knowledge DistillationMachine TranslationNMTTranslation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
Machine Translation IWSLT2014 German-English Data Diversification BLEU score 37.2 #12 of 34 Archive leaderboard report
Machine Translation WMT2014 English-German Data Diversification - Transformer BLEU score 30.7 #9 of 91 Archive leaderboard report

Ranks are positions in the archive's leaderboards as they stood at the 2025-07-28 snapshot. Results published since then are not among these rows, so a rank here is not a current standing.

Methods

Knowledge Distillation

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