[PDF] Top 20 Improving Low Resource Neural Machine Translation with Filtered Pseudo Parallel Corpus
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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
9
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... existing parallel corpora for simu- lating human workers: The MEDAR 1 and Glob- alVoices dataset (Tiedemann, 2012) are consid- ered as new target domains which mainly con- cern the domain of climate change and ... See full document
10
Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
... in translation accuracy of low-resource lan- guages (LRL) (Zoph et ...training corpus is smaller, us- ing a single language is also substantially faster ... See full document
6
Copied Monolingual Data Improves Low Resource Neural Machine Translation
... of parallel data where monolingual data has the most ...relatively low- resource language pairs of English↔Turkish and English ↔ Romanian, we find that our copying technique is effective both alone ... See full document
9
Adaptive Knowledge Sharing in Multi Task Learning: Improving Low Resource Neural Machine Translation
... English-Farsi corpus has ∼105K sentence ...TED corpus (Tiedemann, 2012), accompanied by all the parallel news text in LDC2016E93 Farsi Representative Lan- guage Pack from the Linguistic Data Consor- ... See full document
6
Improving Neural Machine Translation Using Noisy Parallel Data through Distillation
... the machine learning literature, various meth- ods have been proposed for efficient learning with label ...back- translation is expensive as it requires the genera- tion of pseudo source sentences ... See full document
10
Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation
... the low-resource NMT to explicitly utilize the source-side linguistic knowledge, which models the word sequence in parallel to the linguistic features by using two separate encoders with parameter ... See full document
9
Data Augmentation for Low Resource Neural Machine Translation
... Given a source and target sentence pair (S,T), we want to alter it in a way that preserves the semantic equivalence between S and T while diversifying as much as possible the training examples. A number of ways to do ... See full document
7
Neural Machine Translation of Low Resource and Similar Languages with Backtranslation
... large parallel data is ...pervised machine translation where authors have shown that, up to a certain amount of bitext, bet- ter translation systems can be trained with these unsupervised ... See full document
12
Exploiting Out of Domain Parallel Data through Multilingual Transfer Learning for Low Resource Neural Machine Translation
... domain parallel corpus and hence is extremely ...in-domain parallel corpora, ...prominent low-resource techniques, such as mul- tilingual modeling, back-translation, and pivot- ... See full document
12
Universal Neural Machine Translation for Extremely Low Resource Languages
... Ro-En corpus with 6k ...of parallel corpora completely fails to train a vanilla NMT ...Ro-En translation performance gets a substantial improvement which, however, is still limited to be ... See full document
11
Improving a Multi Source Neural Machine Translation Model with Corpus Extension for Low Resource Languages
... 3 corpus consisting of 150,000 ...additional corpus size in training a multi-source ...additional corpus with an initial baseline production ...multi-source corpus which consisted of ... See full document
5
Diversify and Combine: Improving Word Alignment for Machine Translation on Low Resource Languages
... statistical machine translation (SMT) ...The resource required for this approach is little, compared to what is needed to build a rea- sonable discriminative alignment model, for ex- ...on ... See full document
5
Two Ways to Use a Noisy Parallel News Corpus for Improving Statistical Machine Translation
... Statistical Machine Translation (SMT), systems are created from parallel corpora consisting of a set of source language texts aligned with its translation in the target ...which ... See full document
8
Improving Neural Machine Translation with Neural Syntactic Distance
... Based on how the syntactic information is represented, there are two categories of syn- tactic NMT methods: (1) those that use tree- structured neural networks (NNs) to represent syn- tax structures (Eriguchi et ... See full document
6
QCRI MES Submission at WMT13: Using Transliteration Mining to Improve Statistical Machine Translation
... at corpus-level and provides better feature weights that leads to an improvement in translation quality (Nakov et ...allel corpus provided by the ...the parallel corpus, tuning set, ... See full document
6
Sentence Level Adaptation for Low Resource Neural Machine Translation
... of parallel data and quickly learning from aligned translations without pre-defined lin- guistic ...statistical machine translation (SMT) (Koehn et ...large parallel corpora for most language ... See full document
9
Application of Clause Alignment for Statistical Machine Translation
... the parallel resources is of cru- cial importance to the performance of SMT sys- tems and substantial research is focused on devel- oping good parallel corpora of high ...of resource- free against ... See full document
9
Overcoming the Rare Word Problem for low resource language pairs in Neural Machine Translation
... In this study, we have proposed three difference strategies to handle rare words in NMT, in which the combination of methods brings significant im- provements to the NMT systems on two low- resource ... See full document
8
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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