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).
This paper presents a machine translation system based on a combination of a neural machine translation system (NMT) and a context-based machine translation (CBMT). The method is evaluated on a small parallel corpus application of English-Amharic translation. The idea is that in the small corpus setting, the CBMT can leverage a manually built bilingual dictionary
to improve on the standard NMT.  
Clarity: The paper is reasonably clear, though there are numerous typos and minor language glitches (missing articles, etc.). Overall the quality of the writing is probably a bit below what's acceptable for publication at ICLR, but nothing that could not be fixed on subsequent revisions.
Novelty: The method proposed appears to be a fairly straightforward variant of one proposed in a previous paper, where an NMT system was combined with a phrase-based MT system (Zoph & Knight, 2016). There seems to be no novel machine learning contribution (nor is it claimed). This paper seems more appropriate for a venue more focused on machine translation rather than a machine learning venue such as ICLR.
Empirical evidence in support of the claims: The authors set out to demonstrate that by combining a CBMT output into an NMT approach, one can get the best of both approaches. Their results do not strongly support this claim. The results suggest that in the context of the small-scale experiments considered, the baseline CBMT model is actually overall the best performing model. It is therefore strange that, in their last sentence of the conclusion, the authors persist in claiming that their combination "outperforms a simple NMT and a basic CBMT system". That being said, the sub-claim that the
NMT/CBMT hybrid improves on the baseline NMT system is well established. 
In light of the relatively low novelty and the lack of compelling empirical performance for the proposed combined MT system, I do not feel that this paper is appropriate for ICLR at this time.