[PDF] Top 20 Copied Monolingual Data Improves Low Resource Neural Machine Translation
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Copied Monolingual Data Improves Low Resource Neural Machine Translation
... coder. Monolingual data was then introduced by adding an autoencoder ...parallel data to pre-train source → target and target→source NMT systems; they then added monolingual data to the ... See full document
9
Revisiting Low Resource Neural Machine Translation: A Case Study
... While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field (Sutskever et ...highly ... See full document
11
Neural Machine Translation of Low Resource and Similar Languages with Backtranslation
... the data provided data from the ...(new) data sets, as well as the Wiki Titles corpus (Bojar et ...Gnome data sets available through Tiedemann ...titles data set either as this corpus ... See full document
12
Unsupervised Source Hierarchies for Low Resource Neural Machine Translation
... Most work on adding source hierarchical informa- tion to neural machine translation has used super- vised syntax. Luong et al. (2016) used a multi-task setup with a shared encoder to parse and trans- ... See full document
7
The IIIT H Gujarati English Machine Translation System for WMT19
... on low resource NMT has focused on exploiting monolingual data or parallel data from other language ...lingual data ranges from back-translation (Sen- nrich et ...for ... See full document
5
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... Several approaches are proposed for domain adaptation in the context of statistical and neural machine translation. Wang et al. (2017) show a way to adapt existing corpora to new domains us- ing ... See full document
10
Target Conditioned Sampling: Optimizing Data Selection for Multilingual Neural Machine Translation
... examined data selection from the view of domain adaptation, selecting good train- ing data from out-of-domain text to improve in- domain ...select data that score above a preset threshold ac- cording ... See full document
6
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
9
Sentence Level Adaptation for Low Resource Neural Machine Translation
... parallel data and quickly learning from aligned translations without pre-defined lin- guistic ...statistical machine translation (SMT) (Koehn et ...of resource-rich lan- guages, and in limited ... See full document
9
Universal Neural Machine Translation for Extremely Low Resource Languages
... based on the same Ro-En corpus with 6k sentences. As shown in Table 3, it is obvious that 6k sen- tences of parallel corpora completely fails to train a vanilla NMT model. Using Multi-NMT with the as- sistance of 7.8M ... See full document
11
Trivial Transfer Learning for Low Resource Neural Machine Translation
... high- resource pair but the very same EN-ET corpus with source and target ...the data together and added an artificial token indicating the target ... See full document
9
Multimodal Neural Machine Translation for Low resource Language Pairs using Synthetic Data
... Although English(En) and Hindi(Hi) lan- guages belong to the same family (Indo- European), they differ significantly in terms of word order, syntax and morphological struc- ture (Bharati et al., 1995). While English ... See full document
10
Overcoming the Rare Word Problem for low resource language pairs in Neural Machine Translation
... In our previous experiments for English→Vietnamese, BPE algorithm (Sen- nrich et al., 2016b) applied to the source side does not significantly improves the systems despite it is able to reduce the number of ... See full document
8
Improving Low Resource Neural Machine Translation with Filtered Pseudo Parallel Corpus
... the translation model (Wang et ...better translation performance and reduce time- complexity with a small high-quality ...filters data by calculating similarity be- tween source and target ...between ... See full document
9
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
... hance machine translation on both SMT and NMT ...training data to the two different machine translation methods and to the overall ...two machine translation meth- ods on ... See full document
8
Transfer Learning across Low Resource, Related Languages for Neural Machine Translation
... To increase the overlap between the parent and child vocabularies, we use BPE to break words into subwords. For the BPE merge rules to not only find the common subwords between two source languages but also ensure ... See full document
6
Approaching Neural Grammatical Error Correction as a Low Resource Machine Translation Task
... Previously, neural methods in grammatical er- ror correction (GEC) did not reach state-of- the-art results compared to phrase-based sta- tistical machine translation (SMT) ...between neural ... See full document
12
Adaptively Scheduled Multitask Learning: The Case of Low Resource Neural Machine Translation
... Neural Machine Translation (NMT), a data- hungry technology, suffers from the lack of bilingual data in low-resource ...training data of the main and auxiliary ... See full document
10
Bi Directional Differentiable Input Reconstruction for Low Resource Neural Machine Translation
... random translation selection when β = ...the data distribution better (Ott et ...random translation selec- tion introduces lower quality samples and there- fore noisier training ...in ... See full document
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