[PDF] Top 20 Meta Learning for Low Resource Neural Machine Translation
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Meta Learning for Low Resource Neural Machine Translation
... applying meta-learning for low resource machine translation is that the ap- proach outlined above assumes the input and out- put spaces are shared across all the source and tar- ... See full document
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Overcoming the Rare Word Problem for low resource language pairs in Neural Machine Translation
... useful resource for many tasks of natural language processing (Kolte and Bhirud, 2008; Méndez ...the learning synonymous algorithm (called LSW) from the WordNet of English and Japanese to handle unknown ... See full document
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Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
... Prior work has examined data selection from the view of domain adaptation, selecting good train- ing data from out-of-domain text to improve in- domain performance. In general, these methods select data that score above ... See full document
6
Copied Monolingual Data Improves Low Resource Neural Machine Translation
... Ondˇrej Bojar, Rajen Chatterjee, Christian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, An- tonio Jimeno Yepes, Philipp Koehn, Varvara Lo- gacheva, Christof Monz, Matteo Negri, Aur´elie N´ev´eol, Mariana Neves, ... See full document
9
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
6
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
12
Bi Directional Differentiable Input Reconstruction for Low Resource Neural Machine Translation
... in low-resource set- tings by introducing a differentiable recon- struction loss for neural machine translation ...translating translation hypotheses into the in- put ...both ... See full document
7
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
10
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
6
Improving Low Resource Neural Machine Translation with Filtered Pseudo Parallel Corpus
... Data filtering is often used in domain adap- tation (Moore and Lewis, 2010; Axelrod et al., 2011) and phrase-based SMT systems. Sen- tences are extracted from large corpora to opti- mize the language model and the ... See full document
9
Zero Resource Translation with Multi Lingual Neural Machine Translation
... gual neural translation model by Firat et ...language learning (Odlin, 1989; Ringbom, 2007), in machine ...multi-source translation (Zoph and Knight, 2016), as it does not assume the ... See full document
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Exploiting Out of Domain Parallel Data through Multilingual Transfer Learning for Low Resource Neural Machine Translation
... In this paper, we work on a linguisti- cally distant and thus challenging language pair Japanese ↔ Russian (Ja ↔ Ru) which has only 12k lines of news domain parallel corpus and hence is extremely resource-poor. ... See full document
12
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
8
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
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
11
Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
... Ondrej Bojar, Christian Buck, Rajen Chatterjee, Chris- tian Federmann, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, Julia Kreutzer, Varvara Logacheva, Christof Monz, Matteo Negri, ... See full document
12
Adaptively Scheduled Multitask Learning: The Case of Low Resource Neural Machine Translation
... improving low-resource NMT with auxiliary linguistic ...of low-resource NMT, it has been shown that dynamically changing the weights throughout the training is essential to make better use of ... See full document
10
Naive Regularizers for Low-Resource Neural Machine Translation
... Philipp Koehn, Hieu Hoang, Alexandra Birch, Chris Callison-Burch, Marcello Federico, Nicola Bertoldi, Brooke Cowan, Wade Shen, Christine Moran, Richard Zens, Chris Dyer, Ondˇrej Bojar, Alexan- dra Constantin, and Evan ... See full document
10
Data Augmentation for Low Resource Neural Machine Translation
... back- translation in all test ...costly translation process. Improvements are consistent across both translation directions, regardless of whether rare word substitutions are first applied to the ... See full document
7
Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation
... Turkish machine translation tasks are shown in Table 5 and Table 6, ...Turkish→English machine translation task, we can see from Table 5 that our proposed multi-source NMT model outperforms ... See full document
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