Low-Resource Neural Machine Translation
19 papers with code • 1 benchmarks • 5 datasets
Low-resource machine translation is the task of machine translation on a low-resource language where large data may not be available.
Benchmarks
These leaderboards are used to track progress in Low-Resource Neural Machine Translation
| Trend | Dataset | Best Model | Paper | Code | Compare |
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ASPEC
FLoRes
Tatoeba Translation Challenge
Most implemented papers
Revisiting Low-Resource Neural Machine Translation: A Case Study
It has been shown that the performance of neural machine translation (NMT) drops starkly in low-resource conditions, underperforming phrase-based statistical machine translation (PBSMT) and requiring large amounts of auxiliary data to achieve competitive results.
Transfer Learning for Low-Resource Neural Machine Translation
Ensembling and unknown word replacement add another 2 Bleu which brings the NMT performance on low-resource machine translation close to a strong syntax based machine translation (SBMT) system, exceeding its performance on one language pair.
Data Augmentation for Low-Resource Neural Machine Translation
marziehf/DataAugmentationNMT
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ACL 2017
The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora.
Improving Low-Resource Neural Machine Translation with Filtered Pseudo-Parallel Corpus
Large-scale parallel corpora are indispensable to train highly accurate machine translators.
Bi-Directional Differentiable Input Reconstruction for Low-Resource Neural Machine Translation
xingniu/sockeye
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NAACL 2019
We aim to better exploit the limited amounts of parallel text available in low-resource settings by introducing a differentiable reconstruction loss for neural machine translation (NMT).
Effective Cross-lingual Transfer of Neural Machine Translation Models without Shared Vocabularies
yunsukim86/sockeye-transfer
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ACL 2019
Transfer learning or multilingual model is essential for low-resource neural machine translation (NMT), but the applicability is limited to cognate languages by sharing their vocabularies.
Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation
This paper proposes a novel multilingual multistage fine-tuning approach for low-resource neural machine translation (NMT), taking a challenging Japanese--Russian pair for benchmarking.
Improving Back-Translation with Uncertainty-based Confidence Estimation
THUNLP-MT/UCE4BT
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IJCNLP 2019
While back-translation is simple and effective in exploiting abundant monolingual corpora to improve low-resource neural machine translation (NMT), the synthetic bilingual corpora generated by NMT models trained on limited authentic bilingual data are inevitably noisy.
Low Resource Neural Machine Translation: A Benchmark for Five African Languages
Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks.
Language Model Prior for Low-Resource Neural Machine Translation
cbaziotis/lm-prior-for-nmt
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EMNLP 2020
A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data.
