[PDF] Top 20 Morphological Word Embeddings for Arabic Neural Machine Translation in Low Resource Settings
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Morphological Word Embeddings for Arabic Neural Machine Translation in Low Resource Settings
... Neural machine translation has achieved im- pressive results in the last few years, but its success has been limited to settings with large amounts of parallel ...lower-resource ... See full document
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ReWE: Regressing Word Embeddings for Regularization of Neural Machine Translation Systems
... of neural machine translation is still a significant problem, especially in low-resource ...regressing word embeddings (ReWE) as a new regularization technique in a system ... See full document
7
Revisiting Low Resource Neural Machine Translation: A Case Study
... in low-data settings, and can outper- form PBSMT with far less parallel training data than previously ...in low-resource MT research has been the bet- ter exploitation of monolingual and ... See full document
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Exploiting Linguistic Knowledge for Low-Resource Neural Machine Translation
... the low-resource and morphologically-rich ...English machine translation task, the main problem is that the source-side Turkish is a morphologically-rich language with derivational morphology ... See full document
9
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
8
Transfer Learning for Low Resource Neural Machine Translation
... language embeddings for the child model with randomly-assigned embeddings from the par- ent (which has a different input ...child embeddings with similar parent embeddings, where similarity is ... See full document
8
Addressing word order Divergence in Multilingual Neural Machine Translation for extremely Low Resource Languages
... the assisting source language. Bengali, Gujarati, Marathi, Malayalam and Tamil are the source lan- guages, and translation from these to Hindi consti- tute the child tasks. Hindi, Bengali, Gujarati and Marathi are ... See full document
6
When and Why Are Pre Trained Word Embeddings Useful for Neural Machine Translation?
... pre-trained embeddings in providing a better representations of less frequent concepts when used with low-resource ...trained embeddings substitutes these phrases for common ones (“i”), drops ... See full document
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Statistical Machine Translation in Low Resource Settings
... Recently, Klementiev et al. (2012b) induced dis- tributed representations for the crosslingual setting. There, the induced embedding is learned jointly over multiple languages so that the representations of se- ... See full document
8
Morphology aware Word Segmentation in Dialectal Arabic Adaptation of Neural Machine Translation
... whether morphological word segmentation still has an advantage over language agnostic meth- ods, in the context of leveraging parallel data in a resource-rich language to improve the MT of a related ... See full document
7
Using Word Vectors to Improve Word Alignments for Low Resource Machine Translation
... chine translation (MT), especially in low-resource settings where neural MT systems often do not compete with phrase-based and syntax-based MT (Koehn and Knowles, ...used word ... See full document
5
Incremental Domain Adaptation for Neural Machine Translation in Low Resource Settings
... generic neural machine trans- lation model with limited resources ...sentence embeddings to embeddings from the generic ...chine translation experiments (Ar-to-En direc- tion) for ... See full document
10
Trivial Transfer Learning for Low Resource Neural Machine Translation
... in translation direction of the parent and ...shares word embeddings for the source and target ...English word embeddings, but definitely not due to a better English language ...the ... See full document
9
Paraphrasing Out of Vocabulary Words with Word Embeddings and Semantic Lexicons for Low Resource Statistical Machine Translation
... We conducted our experiments on the OLYMPICS task of IWSLT 2012 (Federico et al., 2012). The OLYMPICS task is carried out using parts of the HIT Olympic Trilin- gual Corpus (HIT) (Yang et al., 2006) and the Basic Travel ... See full document
5
Unsupervised Source Hierarchies for 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
7
NICT’s participation to WAT 2019: Multilingualism and Multi step Fine Tuning for Low Resource NMT
... Tamil translation and Russian–Japanese trans- ...back- translation for Russian–Japanese translation and on simple fine-tuning for English–Tamil translation ...extremely ... See full document
5
Proceedings of the 3rd Workshop on Neural Generation and Translation
... Machine Translation of Restaurant Reviews: New Corpus for Domain Adaptation and Robustness Alexandre Berard, Ioan Calapodescu, Marc Dymetman, Claude Roux, Jean-Luc Meunier and Vas- silina Nikoulina ...of ... See full document
16
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
Supervised Phrase Table Triangulation with Neural Word Embeddings for Low Resource Languages
... phrase translation scores obtained by phrase table ...extract word translation distributions from small amounts of source-target bilin- gual data (a dictionary or a parallel corpus) with which we ... See full document
5
Word Embeddings and Convolutional Neural Network for Arabic Sentiment Classification
... the Arabic language is still recognized at its early stages (Nabil et ...on Arabic is considered as a more challenging work. Firstly, Arabic has a very complex morphology and ...of Arabic ... See full document
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