Context Based Machine Translation With Recurrent Neural Network for English - Amharic Translation
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Date
2020-02
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Addis Ababa University
Abstract
The quote from Rev. Jesse Jackson, \A text without a context is a pretext",
summarizes the reasoning behind this thesis. Capturing context in translating
between two human languages using computing machines is challenging.
It is more challenging when the languages di er greatly in grammar and have
small parallel corpus like the English-Amharic pair. The current approaches
for English-Amharic machine translation usually require large set of parallel
corpus in order to achieve
uency as in the case of statistical machine translation
(SMT) and example based machine translation (EBMT). The context
awareness of phrase based machine translation (PBMT) approaches used for
the pair so far are also questionable. This research develops a system that
translates English text to Amharic text using a combination of context based
machine translation (CBMT) and a recurrent neural network machine translation
(RNNMT). We built a bilingual dictionary for the CBMT system to
use along with a target corpus. The RNNMT model has then been provided
with the output of the CBMT and a parallel corpus for training. The proposed
approach is evaluated using the New Testament Bible as a corpus. The
result shows that the combinational approach on English-Amharic language
pair yields a performance improvement of 2.805 BLEU scores on average over
basic neural machine translation(NMT).
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Keywords
Machine Translation, context based machine translation, English to Amharic translation, recurrent neural network machine translation, context based machine translation with neural network machine translation