The paper by Ashengo et al. proposes a context-based MT (CBMT) with RNN approach to build English-Amharic models. Amharic is a morphologically rich language and requires larger parallel corpus to train MT systems to achieve comparable performance. They followed the setup proposed by Niehues et al. (2016) and Zhou et al. (2017) to first use CBMT to translate source text into CBMT target text, which is then combined with the original source text as the input of NMT system. The CBMT system takes advantage of bilingual dictionary, a large target corpus and synonym finder to translate the source into CBMT target text. The resulting NMT model is trained using source and CBMT target as input, and target text as output. The approach alleviates the need of large corpora for MT training. It would be interesting to investigate how it performs in other morphologically rich languages and low-resource languages.
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