Context Based Machine Translation With Recurrent Neural Network For English-Amharic Translation

Yeabsira Asefa Ashengo, Rosa Tsegaye Aga, Surafel Lemma Abebe

Keywords: Context based machine translation, machine translation, Neural network machine translation, English to Amharic machine translation

TL;DR: A context based machine translation system combined with a recurrent neural network machine translation system to translate English to Amharic.

Abstract: The current approaches for machine translation usually require large set of parallel corpus in order to achieve fluency like in the case of neural machine translation (NMT), statistical machine translation (SMT) and example-based machine translation (EBMT). The context awareness of phrase-based machine translation (PBMT) approaches is 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 large target corpus. The RNNMT model has then been provided with the output of the CBMT and a parallel corpus for training. Our combinational approach on English-Amharic language pair yields a performance improvement over the simple neural machine translation (NMT).