Linked Data Effectiveness in Neural Machine Translation

2020 ◽  
Author(s):  
Benyamin Ahmadnia
Keyword(s):  
Machine Translation ◽  
Linked Data ◽  
Neural Machine Translation

scholarly journals Semantic and syntactic information for neural machine translation

2021 ◽  
Author(s):  
Jordi Armengol-Estapé ◽  
Marta R. Costa-jussà
Keyword(s):  
Machine Translation ◽  
Linked Data ◽  
Semantic Information ◽  
Semantic Features ◽  
Linguistic Features ◽  
Neural Machine Translation ◽  
Low Resource ◽  
Syntactic Information ◽  
Attentional System

AbstractIntroducing factors such as linguistic features has long been proposed in machine translation to improve the quality of translations. More recently, factored machine translation has proven to still be useful in the case of sequence-to-sequence systems. In this work, we investigate whether this gains hold in the case of the state-of-the-art architecture in neural machine translation, the Transformer, instead of recurrent architectures. We propose a new model, the Factored Transformer, to introduce an arbitrary number of word features in the source sequence in an attentional system. Specifically, we suggest two variants depending on the level at which the features are injected. Moreover, we suggest two combination mechanisms for the word features and words themselves. We experiment both with classical linguistic features and semantic features extracted from a linked data database, and with two low-resource datasets. With the best-found configuration, we show improvements of 0.8 BLEU over the baseline Transformer in the IWSLT German-to-English task. Moreover, we experiment with the more challenging FLoRes English-to-Nepali benchmark, which includes both low-resource and very distant languages, and obtain an improvement of 1.2 BLEU. These improvements are achieved with linguistic and not with semantic information.

scholarly journals An Empirical Study on Learning Bug-Fixing Patches in the Wild via Neural Machine Translation

2019 ◽  
Vol 28 (4) ◽  
pp. 1-29 ◽  
Author(s):  
Michele Tufano ◽  
Cody Watson ◽  
Gabriele Bavota ◽  
Massimiliano Di Penta ◽  
Martin White ◽  
...  
Keyword(s):  
Empirical Study ◽  
Machine Translation ◽  
Neural Machine Translation ◽  
Bug Fixing ◽  
In The Wild

scholarly journals Neural Machine Translation for Semantic-Driven Q&A Systems in the Factory Planning

Procedia CIRP ◽  
2021 ◽  
Vol 96 ◽  
pp. 9-14
Author(s):  
Uwe Dombrowski ◽  
Alexander Reiswich ◽  
Raphael Lamprecht
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Factory Planning

Neural machine translation with a polysynthetic low resource language

2020 ◽  
Vol 34 (4) ◽  
pp. 325-346
Author(s):  
John E. Ortega ◽  
Richard Castro Mamani ◽  
Kyunghyun Cho
Keyword(s):  
Machine Translation ◽  
Neural Machine Translation ◽  
Low Resource
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