[PDF] Top 20 Transfer Learning for Low Resource Neural Machine Translation
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Transfer Learning for Low Resource Neural Machine Translation
... Transfer learning uses knowledge from a learned task to improve the performance on a related task, typically reducing the amount of required training data (Torrey and Shavlik, 2009; Pan and Yang, ... See full document
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Exploiting Out of Domain Parallel Data through Multilingual Transfer Learning for Low Resource Neural Machine Translation
... for low-resource neural machine translation (NMT), taking a challenging Japanese– Russian pair for ...for low- resource scenarios, such as multilingual NMT and ... See full document
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Trivial Transfer Learning for Low Resource Neural Machine Translation
... Furthermore, the improvement is not restricted only to related languages as Estonian and Finnish as shown in previous works. Unrelated language pairs (shown in bold in Table 2) like Czech and Estonian work too and in ... See full document
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Meta Learning for Low Resource Neural Machine Translation
... for low- resource neural machine translation ...frame low-resource translation as a meta- learning problem, and we learn to adapt to ... See full document
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Overcoming the Rare Word Problem for low resource language pairs in Neural Machine Translation
... of neural machine translation (NMT) coined by (Koehn and Knowles, 2017), rare-word problem is consid- ered the most severe one, especially in trans- lation of low-resource ...in ... See full document
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An Empirical Study of Language Relatedness for Transfer Learning in Neural Machine Translation
... most resource abundant ones and hence the gains to the transfer learning (less than 1 BLEU point) are notable only in cases where the baseline sys- tems are not that ... See full document
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Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
... Multilingual NMT has led to impressive gains in translation accuracy of low-resource lan- guages (LRL) (Zoph et al., 2016; Firat et al., 2016; Gu et al., 2018; Neubig and Hu, 2018; Nguyen and Chiang, ... See full document
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Japanese Russian TMU Neural Machine Translation System using Multilingual Model for WAT 2019
... Asian Translation (Nakazawa et ...news translation. It is a very challenging task considering: (a) extremely low resource setting, the size of parallel data is only 12k parallel sentences; (b) ... See full document
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Copied Monolingual Data Improves Low Resource Neural Machine Translation
... relatively low- resource language pairs of English↔Turkish and English ↔ Romanian, we find that our copying technique is effective both alone and combined with ...in low-resource settings, ... See full document
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Improving Back Translation with Uncertainty based Confidence Estimation
... improve low-resource neural machine translation (NMT), the synthetic bilingual cor- pora generated by NMT models trained on limited authentic bilingual data are inevitably ... See full document
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Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... active learning in NMT using our advanced sentence sampling on translation time and quality of incremental model ...post-edit translation candidates, translations that improve over time might reduce ... See full document
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Adaptive Knowledge Sharing in Multi Task Learning: Improving Low Resource Neural Machine Translation
... • The English-Vietnamese has ∼ 133K training pairs. It is the preprocessed version of the IWSLT 2015 translation task provided by (Lu- ong and Manning, 2015). It consists of sub- titles and their corresponding ... See full document
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Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
... Junczys-Dowmunt and Grundkiewicz (2016) no- ticed that when tuning on the entire NUCLE cor- pus, even better results can be achieved if the error rate of NUCLE is adapted to the error rate of the original dev set. In ... See full document
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Bi Directional Differentiable Input Reconstruction for Low Resource Neural Machine Translation
... random translation selection when β = ...random translation selec- tion introduces lower quality samples and there- fore noisier training ...in low-resource set- tings (Edunov et ... See full document
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Improving Low Resource Neural Machine Translation with Filtered Pseudo Parallel Corpus
... than sent-BLEU. In contrast, using sent-BLEU in- creased performance even when much less data were used for training. The “sent-BLEU ≥ 0.3” model outperformed the “Unfiltered” model by +3.77 and +2.64 points on the ... See full document
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Hunter NMT System for WMT18 Biomedical Translation Task: Transfer Learning in Neural Machine Translation
... empirical study of efficient training on multiple in-domain and out-of-domain datasets. We ap- plied transfer learning by training NMT systems with different datasets one after another carrying on the ... See full document
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Investigating Phrase-Based and Neural-Based Machine Translation on Low-Resource Settings
... and English-Chinese (the UM-Corpus (Tian et al., 2014)). Nevertheless, such large bilingual corpora are unavailable for most language pairs in the world (Irvine, 2013; Wang et al., 2016), which causes a bottleneck for ... See full document
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Morphological Word Embeddings for Arabic Neural Machine Translation in Low Resource Settings
... Other work uses purely unsupervised techniques. Luong et al. (2013) segment words using Morfes- sor (Creutz and Lagus, 2007), and use recursive neural networks to build word embeddings from morph embeddings. ... See full document
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Sentence Level Adaptation for Low Resource Neural Machine Translation
... quickly learning from aligned translations without pre-defined lin- guistic ...statistical machine translation (SMT) (Koehn et ...of resource-rich lan- guages, and in limited domains such as ... See full document
9
Data Augmentation for Low Resource Neural Machine Translation
... As NMT system we use a 4-layer attention- based encoder-decoder model as described in (Lu- ong et al., 2015) trained with hidden dimension 1000, batch size 80 for 20 epochs. In all experi- ments the NMT vocabulary is ... See full document
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