Low-resource machine translation is the task of machine translation on a low-resource language where large data may not be available.
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.
The quality of a Neural Machine Translation system depends substantially on the availability of sizable parallel corpora.
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.
A common solution is to exploit the knowledge of language models (LM) trained on abundant monolingual data.
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.
Recent advents in Neural Machine Translation (NMT) have shown improvements in low-resource language (LRL) translation tasks.
Large-scale parallel corpora are indispensable to train highly accurate machine translators.
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.