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An Effective Optimization Method for Neural Machine Translation: The Case of English-Persian Bilingually Low-Resource Scenario
In this paper, we propose a useful optimization method for low-resource Neural Machine Translation (NMT) by investigating the effectiveness of multiple neural network optimization algorithms.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
+2
Bilingual Low-Resource Neural Machine Translation with Round-Tripping: The Case of Persian-Spanish
The quality of Neural Machine Translation (NMT), as a data-driven approach, massively depends on quantity, quality, and relevance of the training dataset.
Low Resource Neural Machine Translation
Low-Resource Neural Machine Translation
+3
Enhancing Phrase-Based Statistical Machine Translation by Learning Phrase Representations Using Long Short-Term Memory Network
Analytically, the performance of a PBSMT system is enhanced by using the conditional probabilities of phrase pairs computed by an LSTM encoder-decoder as an additional feature in the existing log-linear model.
Persian-Spanish Low-Resource Statistical Machine Translation Through English as Pivot Language
This paper is an attempt to exclusively focus on investigating the pivot language technique in which a bridging language is utilized to increase the quality of the Persian-Spanish low-resource Statistical Machine Translation (SMT).
