Original: C ONTEXT
Revised: C ONTEXT

Original: B ASED
Revised: B ASED

Original: M ACHINE
Revised: M ACHINE

Original: T RANSLATION
Revised: T RANSLATION

Original: WITH
Revised: WITH

Original: R ECURRENT
Revised: R ECURRENT

Original: N EURAL
Revised: N EURAL

Original: N ETWORK
Revised: N ETWORK

Original: FOR
Revised: FOR

Original: E NGLISHA MHARIC T RANSLATION
Revised: E NGLISHA MHARIC T RANSLATION

Original: Anonymous authors
Revised: Anonymous authors

Original: Paper under double-blind review
Revised: Paper under double-blind review

Original: A BSTRACT
Revised: A BSTRACT

Original: 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).
Revised: 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).

Original: The context awareness of phrase-based machine translation (PBMT) approaches is also questionable.
Revised: The context awareness of phrase-based machine translation (PBMT) approaches is also questionable.

Original: 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).
Revised: 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).

Original: We built a bilingual dictionary for the CBMT system to use along with a large target corpus.
Revised: We built a bilingual dictionary for the CBMT system to use along with a large target corpus.

Original: The RNNMT model has then been provided with the output of the CBMT and a parallel corpus for training.
Revised: The RNNMT model has then been provided with the output of the CBMT and a parallel corpus for training.

Original: Our combinational approach on English-Amharic language pair yields a performance improvement over the simple neural machine translation (NMT).
Revised: Our combinational approach on English-Amharic language pair yields a performance improvement over the simple neural machine translation (NMT).

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Context based machine translation (CBMT) is anew phrase-based machine translation (PBMT) approach proposed by Miller et al.
Revised: Context based machine translation (CBMT) is a phrase-based machine translation (PBMT) approach proposed by Miller et al.

Original: Unlike most PBMT approaches that rely on statistical occurrence of the phrases, CBMT works on the contextual occurrence of the phrases.
Revised: Unlike most PBMT approaches that rely on statistical occurrence of the phrases, CBMT works on the contextual occurrence of the phrases.

Original: CBMT uses bilingual dictionary as its main translator and produces phrases to be ﬂooded into a large target corpus.
Revised: CBMT uses bilingual dictionary as its main translator and produces phrases to be ﬂooded into a large target corpus.

Original: The CBMT approach addresses the problem of parallel corpus scarcity between language pairs.
Revised: The CBMT approach addresses the problem of parallel corpus scarcity between language pairs.

Original: The parallel corpus set for English-Amharic language pair, for instance, composes of the Bible, the
Revised: The parallel corpus set for English-Amharic language pair, for instance, composes of the Bible, the

Original: Ethiopian constitution and international documents.
Revised: Ethiopian constitution and international documents.

Original: These sources use words speciﬁc to their domain and overlook phrases and words used by novels, news and similar literary documents.
Revised: These sources use words speciﬁc to their domain and overlook phrases and words used by novels, news and similar literary documents.

Original: The
Revised: The

Original: CBMT uses synonyms of words in place of rare words and rely on large target corpus and a bilingual dictionary to help with data scarcity(Miller et al., 2006).
Revised: CBMT uses synonyms of words in place of rare words and rely on large target corpus and a bilingual dictionary to help with data scarcity(Miller et al., 2006).

Original: It is not dependent on large parallel corpus like most PBMT such as the statistical machine translation (SMT)(Brown et al., 1990) and the example-based machine translation EBMT(Gangadharaiah, 2011).
Revised: It is not dependent on large parallel corpus like most PBMT such as the statistical machine translation (SMT)(Brown et al., 1990) and the example-based machine translation EBMT(Gangadharaiah, 2011).

Original: The CBMT, however, fails in ﬂuently translating texts compared to the neural machine translation (NMT).
Revised: The CBMT, however, fails in ﬂuently translating texts compared to the neural machine translation (NMT).

Original: The NMT learns the pattern of humans’ translation using human translated parallel corpus and translate accordingly.
Revised: The NMT learns the pattern of humans’ translation using human translated parallel corpus.

Original: Its translations are more ﬂuent and accurate than all the rest so far when evaluated individually (Popovic, 2017).
Revised: Its translations are more ﬂuent and accurate than all the rest so far when evaluated individually (Popovic, 2017).

Original: However, NMT struggles to translate properly rare words and words not commonly used(Wu et al., 2016).
Revised: However, NMT struggles to translate properly rare words and words not commonly used(Wu et al., 2016).

Original: In addition, NMT requires large parallel corpus for training.
Revised: In addition, NMT requires large parallel corpus for training.

Original: The aim of this research is to build a system by combining the CBMT with the NMT for English to Amharic translation.
Revised: The aim of this research is to build a system by combining the CBMT with the NMT for English to Amharic translation.

Original: The combination of PBMT and NMT is the future and most promising than the individual approaches themselves (Popovic, 2017).
Revised: The combination of PBMT and NMT is the future and most promising than the individual approaches themselves (Popovic, 2017).

Original: CBMT’s ability to address rare words and the NMT’s ability to produce ﬂuent translation along with their context awareness makes them complementary couple.
Revised: CBMT’s ability to address rare words and the NMT’s ability to produce ﬂuent translation along with their context awareness makes them complementary couple.

Original: The combination is done by providing the NMT with two inputs, one from the source language and the other from the output of the CBMT to produces the ﬁnal target sentence.
Revised: The combination is done by providing the NMT with two inputs, one from the source language and the other from the output of the CBMT to produces the ﬁnal target sentence.

Original: In this paper, we show that this approach utilizes the strength of each method to achieve a signiﬁcant translation perfor- mance improvement over simple NMT.
Revised: In this paper, we show that this approach utilizes the strength of each method to achieve a signiﬁcant translation performance improvement over simple NMT.

Original: The improvement is mostly dependent on the performance of the CBMT and mostly on the bilingual dictionary of the CBMT.
Revised: The improvement is mostly dependent on the performance of the CBMT and mostly on the bilingual dictionary of the CBMT.

Original:  R ELATED W ORKS
Revised:  R ELATED W ORKS

Original: PBMT approaches are mostly used to translate English to Amharic as in the case of Gasser (2012),Tadesse & Mekuria (2000), Teshome (2000), Besacier et al. (2000), Zewgneh (2017) and
Revised: PBMT approaches are mostly used to translate English to Amharic as in the case of Gasser (2012),Tadesse & Mekuria (2000), Teshome (2000), Besacier et al. (2000), Zewgneh (2017) and

Original: Taye et al.
Revised: Taye et al.

Original: Below we summarize the researches with most signiﬁcance to ours.
Revised: Below we summarize the researches with most signiﬁcance to ours.

Original: The SMT approach takes a parallel corpus as an input and it selects the most frequent target phrase based on statistical analysis for each searched source phrase (Brown et al., 1990).
Revised: The SMT approach takes a parallel corpus as an input and it selects the most frequent target phrase based on statistical analysis for each searched source phrase (Brown et al., 1990).

Original: The SMT approach applied to the English-Amharic pair has produced 18.74 % BLUE score (Tadesse & Mekuria, 2000).
Revised: The SMT approach applied to the English-Amharic pair has produced 18.74 % BLEU score (Tadesse & Mekuria, 2000).

Original: The SMT has good accuracy in translating all the words in a sentence but it is not ﬂuent (Oladosu et al., 2016).
Revised: The SMT has good accuracy in translating all the words in a sentence but it is not ﬂuent (Oladosu et al., 2016).

Original: Hybrid of SMT and rule based machine translation (RBMT) translates and orders the source text based on the grammar rules of the target language and sends it to the SMT for ﬁnal translation(Yulianti et al., 2011)(Labaka et al., 2014).
Revised: Hybrid of SMT and rule based machine translation (RBMT) translates and orders the source text based on the grammar rules of the target language and sends it to the SMT for ﬁnal translation(Yulianti et al., 2011)(Labaka et al., 2014).

Original: The hybrid approach for English-Amharic pair has achieved a 15% improvement over SMT on simple sentence and 20% improvement for complex
Revised: The hybrid approach for English-Amharic pair has achieved a 15% improvement over SMT on simple sentence and 20% improvement for complex

Original: sentence(Zewgneh, 2017).
Revised: sentence(Zewgneh, 2017).

Original: Hybrid of RBMT and SMT gets ﬂuency from RBMT and accuracy from
Revised: Hybrid of RBMT and SMT gets ﬂuency from RBMT and accuracy from

Original: SMT but for longer sentences, the reordering fails(Oladosu et al., 2016).
Revised: SMT but for longer sentences, the reordering fails(Oladosu et al., 2016).

Original: The CBMT approach has been implemented for the language pair Spanish-English.
Revised: The CBMT approach has been implemented for the language pair Spanish-English.

Original: In CBMT, the source phrases are translated using bilingual dictionary and ﬂooded to target corpus.
Revised: In CBMT, the source phrases are translated using bilingual dictionary and ﬂooded to target corpus.

Original: It has achieved 64.62% BLUE score for the researchers’ dataset(Miller et al., 2006).
Revised: It has achieved 64.62% BLEU score for the researchers’ dataset(Miller et al., 2006).

Original: The CBMT outperforms SMT in accuracy and ﬂuency but translation of phrases with words not in the bilingual dictionary is weak (Miller et al., 2006).
Revised: The CBMT outperforms SMT in accuracy and ﬂuency but translation of phrases with words not in the bilingual dictionary is weak (Miller et al., 2006).

Original: The NMT has been researched by different groups and here the research by Googles’ researchers on the language pair English-French is presented.
Revised: The NMT has been researched by different groups and here the research by Googles’ researchers on the language pair English-French is presented.

Original: The NMT model is trained using parallel corpus.
Revised: The NMT model is trained using parallel corpus.

Original: The source sentence is encoded as a vector and then decoded with the help of an attention model.
Revised: The source sentence is encoded as a vector and then decoded with the help of an attention model.

Original: Googles’ NMT model has achieved 38.95% BLUE score(Wu et al., 2016).
Revised: Googles’ NMT model has achieved 38.95% BLEU score(Wu et al., 2016).

Original: The NMT has accuracy and ﬂuency but it fails to translate the whole sentence and also fails to perform well with rare words(Wu et al., 2016).
Revised: The NMT has accuracy and ﬂuency but it fails to translate the whole sentence and also fails to perform well with rare words(Wu et al., 2016).

Revised: To solve this using sub-word units has been suggested (Sennrich et al., 2016) but Amharic has unique treats like ”Tebko-lalto”, one word with two different meanings, which can only be addressed by using context.

Revised: The NMT has been modiﬁed to translate low resourced languages.

Revised: One approach uses universal lexical representation (ULR) were a word is represented using universal word embeddings.

Revised: This beneﬁts low resource languages which have semantic similarity with high resourced languages (Gu et al., 2018).

Revised: This achieved 5% BLEU score improvement over normal NMT.

Original:  Combination of NMT and PBMT which takes the output of SMT (a PBMT) and the source sentence to train the NMT model has been used for the language pair English-German.
Revised: However, most southern Semitic languages like Amharic do not have a strong semantic relative with large resource. NMT has also been modiﬁed to work with monolingual corpus instead of parallel corpus using cross-lingual word embedding(Artetxe et al., 2017). Such an approach achieved a 15.56% BLEU score which was less that the semi-supervised and supervised which achieved 21.81% BLEU score and 20.48% BLEU score respectively. Combination of NMT and PBMT which takes the output of SMT (a PBMT) and the source sentence to train the NMT model has been used for the language pair English-German.

Original: It has achieved
Revised: It has achieved

Original: BLUE points over basic NMT and PBMT(Niehues et al., 2016).
Revised: BLEU points over basic NMT and PBMT(Niehues et al., 2016).

Original: Combination of NMT and PBMT which takes three inputs; output of basic NMT, output of SMT and output of Hierarchical PBMT (HPBMT) of SMT has been implemented for English-Chinese language pair.
Revised: Combination of NMT and PBMT which takes three inputs; output of basic NMT, output of SMT and output of Hierarchical PBMT (HPBMT) of SMT has been implemented for English-Chinese language pair.

Original: It achieved 6 BLUE points over basic NMT and 5.3 BLUE points over HPBMT(Zhang et al., 2017).
Revised: It achieved 6 BLEU points over basic NMT and 5.3 BLEU points over HPBMT(Zhang et al., 2017).

Original: The combination of PBMT and NMT performs better (Popovic, 2017) in terms of accuracy and ﬂuency but it is dependent on the performance of the chosen PBMT approach.
Revised: The combination of PBMT and NMT performs better (Popovic, 2017) in terms of accuracy and ﬂuency but it is dependent on the performance of the chosen PBMT approach.

Original:  M ETHODOLOGY
Revised:  M ETHODOLOGY

Original: In this research, we have selected CBMT and NMT to form a combinational system.
Revised: In this research, we have selected CBMT and NMT to form a combinational system.

Original: This approach addresses the limitation of context unawareness of some PBMT approaches like SMT and the need for large parallel corpus of simple NMT.
Revised: This approach addresses the limitation of context unawareness of some PBMT approaches like SMT and the need for large parallel corpus of simple NMT.

Original: In our approach, the source sentence in English and the translation output of the CBMT in Amharic has been fed to the NMT’s encoder-decoder model as shown in Figure 1.
Revised: In our approach, the source sentence in English and the translation output of the CBMT in Amharic has been fed to the NMT’s encoder-decoder model as shown in Figure 1.

Original: The NMT model then produces the ﬁnal Amharic translation based on its training.
Revised: The NMT model then produces the ﬁnal Amharic translation.

Original: The combination of the CBMT and the NMT follows the mixed approach proposed by Niehues et al.
Revised: The combination of the CBMT and the NMT follows the mixed approach proposed by Niehues et al.

Original: Their mixed approach feeds the NMT with the source sentence and the output of the PBMT.
Revised: Their mixed approach feeds the NMT with the source sentence and the output of the PBMT.

Original: The research by Zhang et al.
Revised: The research by Zhang et al.

Original: (2017) also supports this way of combining different systems.
Revised: (2017) also supports this way of combining different systems.

Original: 3. CBMT SYSTEM
Revised: 3. CBMT SYSTEM

Original: The CBMT outperforms RBMT, SMT and EBMT when it comes to languages with less parallel corpora(Miller et al., 2006).
Revised: The CBMT outperforms RBMT, SMT and EBMT when it comes to languages with less parallel corpora(Miller et al., 2006).

Original: It uses a bilingual dictionary, a large target corpus and smaller source corpus, which is optional.
Revised: It uses a bilingual dictionary, a large target corpus and smaller source corpus, which is optional.

Original: In the context based machine translation, there are different components working together to produce the translation.
Revised: In the context based machine translation, there are different components working together to produce the translation.

Original: Figure 2 shows the ﬂow of data in the different components of the CBMT.
Revised: Figure 2 shows the ﬂow of data in the different components of the CBMT.

Original: The source sentence is converted into N-gram phrases and then it is translated using bilingual dictionary.
Revised: The source sentence is converted into N-gram phrases and then it is translated using bilingual dictionary.

Original: CBMT’s performance is mostly dependent on the efﬁciency of the dictionary.
Revised: CBMT’s performance is mostly dependent on the efﬁciency of the dictionary.

Original: We have manually built a phrase based dictionary aided by Google translate.
Revised: We have manually built a phrase based dictionary aided by Google translate.

Original: A synonym ﬁnder helps the dictionary’s search using WordNet(Soergel, 1998).
Revised: A synonym ﬁnder helps the dictionary’s search using WordNet(Soergel, 1998).

Original: WordNet is a library with large lexical database of English words.
Revised: WordNet is a library with large lexical database of English words.

Original: It provides synonyms of the English words whose Amharic translations are not in dictionary.
Revised: It provides synonyms of the English words whose Amharic translations are not in dictionary.

Original: In this paper, a maximum of four N-grams has been used.
Revised: In this paper, a maximum of four N-grams has been used.

Original: Most phrases of English that are translated to a single word in Amharic have a length of four or less words.
Revised: Most phrases of English that are translated to a single word in Amharic have a length of four or less words.

Original: For example the English phrase ”everyone who calls on the name of the lord will be saved” has the translations in Output 1 using our dictionary.
Revised: For example the English phrase ”everyone who calls on the name of the lord will be saved” has the translations in Output 1 using our dictionary.

Original: Output 1: The translated output of the N-grams
Revised: Output 1: The translated output of the N-grams

Original: These translations have been combined into sentences in the same order as the source sentence.
Revised: These translations have been combined into sentences in the same order as the source sentence.

Original: Then each sentence is converted into N-grams of variable length.
Revised: Then each sentence is converted into N-grams of variable length.

Original: The maximum ﬂooded N-grams length is len ( Ngram )+ 1 if len ( Ngram ) ≥ 4 else it is equal to len(Ngram) .
Revised: The maximum ﬂooded N-grams length is len ( Ngram )+ 1 if len ( Ngram ) ≥ 4 else it is equal to len(Ngram) .

Original: This provides a good variable range to capture neighboring words in a single N-gram.
Revised: This provides a good variable range to capture neighboring words in a single N-gram.

Original: Output 2 shows sentences formed using the translations in Output 1.
Revised: Output 2 shows sentences formed using the translations in Output 1.

Original: The N-grams to be ﬂooded are formed by sliding one word from left to right through the combined sentence.
Revised: The N-grams to be ﬂooded are formed by sliding one word from left to right through the combined sentence.

Original:  The translated  sentence
Revised: Output 2: The translated output combined into sentence

Original: Output 3 shows the N-grams for the translated sentences shown in Output 2.
Revised: Output 3 shows the N-grams for the translated sentences shown in Output 2.

Original: Output 
Revised: Output 3: The N-grams for the translated sentences

Original: The ﬂooder is then responsible for searching the translated phrases in the target corpus and ﬁnding the longest N-gram match.
Revised: The ﬂooder is then responsible for searching the translated phrases in the target corpus and ﬁnding the longest N-gram match.

Original: For each phrase to be ﬂooded, it selects a phrase in the target corpus with the most translated words and least in-between words amongst the words matched.
Revised: For each phrase to be ﬂooded, it selects a phrase in the target corpus with the most translated words and least in-between words amongst the words matched.

Original: The ﬂooder produces the result in Output 4 with the Book of Romans as the target corpus to be ﬂooded.
Revised: The ﬂooder produces the result in Output 4 with the Book of Romans as the target corpus to be ﬂooded.

Original: Output 4: Final output of ﬂooder for single ﬂooded ﬁle
Revised: Output 4: Final output of ﬂooder for single ﬂooded ﬁle

Original: The N-gram connector combines the ﬂooded text to ﬁnd the longest overlap of the translated target text.
Revised: The N-gram connector combines the ﬂooded text to ﬁnd the longest overlap of the translated target text.

Original: The overlapping system favors those with the least number of not searched words found in between the searched N-grams when calculating the overlap.
Revised: The overlapping system favors those with the least number of not searched words found in between the searched N-grams when calculating the overlap.

Original: Output 5 shows the ﬁnal outcome of the N-gram connector.
Revised: Output 5 shows the ﬁnal outcome of the N-gram connector.

Original: The system selects the maximum or longest overlapping phrases from the combiner and merges them to form the ﬁnal target sentence.
Revised: The system selects the maximum or longest overlapping phrases from the combiner and merges them to form the ﬁnal target sentence.

Original: So ﬁnally, the translation for the example English phrase ”everyone who calls on the name of the lord will be saved” is .
Revised: So ﬁnally, the translation for the example English phrase ”everyone who calls on the name of the lord will be saved” is .

Original: 3. NMT SYSTEM
Revised: 3. NMT SYSTEM

Original: In this paper, we have used RNN (recurrent neural networks) for the NMT.
Revised: In this paper, we have used RNN (recurrent neural networks) for the NMT.

Original: In RNN, the output is fed back to the neuron to learn from both the fresh input and its previous output.
Revised: In RNN, the output is fed back to the neuron to learn from both the fresh input and its previous output.

Original: This improves RNN’s performance because it learns from its previous mistakes.
Revised: This improves RNN’s performance because it learns from its errors while training.

Original: The neural cell used is the LSTM (long short-term memory), introduced by Hochreiterand Schmidhuber(Hochreiter & Schmidhuber, 1997).
Revised: The neural cell used is the LSTM (long short-term memory), introduced by Hochreiter & Schmidhuber (1997).

Original: We have used LSTM cells for both the encoding and decoding of the sentences.
Revised: We have used LSTM cells for both the encoding and decoding of the sentences.

Original: For the decoding, a greedy algorithm has been used.
Revised: For the decoding, a greedy algorithm has been used.

Original: The algorithm selects the ﬁrst ﬁt word that has the highest probability of occurrence.
Revised: The algorithm selects the ﬁrst ﬁt word that has the highest probability of occurrence.

Original: Probability refers to the probability of being translated and appearing next to the word before itself.
Revised: Probability refers to the probability of being translated and appearing next to the word before itself.

Original: The system has an attention layer between the encoder layer and the decoder layer.
Revised: The system has an attention layer between the encoder layer and the decoder layer.

Original: We have used the
Revised: We have used the

Original: Loung attention model(Luong et al., 2015).
Revised: Luong attention model(Luong et al., 2015).

Original: Equation 1 through Equation 4 shows Loung attention model’s computation.
Revised: Equation 1 through Equation 4 shows Luong attention model’s computation.

Original: [Equation]
Revised: [Equation]

Original: [Attention Weights
Revised: [Attention Weights]

Original: [Equation]
Revised: [Equation]

Original: Output 5: Final

Original: N gram connector

Original: [Equation]
Revised: [Equation]

Original: The score function, calculated using Equation 4, is used to compare the output of the decoder ( h t ) with the output of the encoder ( h s ) in order to ﬁnd the attention weight calculated using Equation 1.
Revised: The score function, calculated using Equation 4, is used to compare the output of the decoder ( h t ) with the output of the encoder ( h s ) in order to ﬁnd the attention weight calculated using Equation 1.

Original: The Attention weights ( alpha ts ) are then used for the context vector( c t ) calculated by Equation 2.
Revised: The Attention weights ( alpha ts ) are then used for the context vector( c t ) calculated by Equation 2.

Original: This context vector as well as the output of the decoder is then used to produce the ﬁnal output of the decoder using Equation 3.
Revised: This context vector as well as the output of the decoder is then used to produce the ﬁnal output of the decoder using Equation 3.

Original: 3. C OMBINATION OF CBMT AND NMT
Revised: 3. C OMBINATION OF CBMT AND NMT

Original: To combine the two systems, we have made the NMT model to accept two inputs.
Revised: To combine the two systems, we have made the NMT model to accept two inputs.

Original: We have used the proposed method of combining PBMT with NMT accepting two source inputs by Zoph & Knight (2016).
Revised: We have used the proposed method of combining PBMT with NMT accepting two source inputs by Zoph & Knight (2016).

Original: According to Zoph & Knight (2016), having two inputs, where one is the source sentence and the other a translation of the source to another language different from the target, helps the NMT produce a better result.
Revised: According to Zoph & Knight (2016), having two inputs, where one is the source sentence and the other a translation of the source to another language different from the target, helps the NMT produce a better result.

Original: The source sentence and the sentence translated using the CBMT are encoded separately and are given to the attention layer.
Revised: The source sentence and the sentence translated using the CBMT are encoded separately and are given to the attention layer.

Original: The attention layer focuses on the two inputs at the same time rather than separately.
Revised: The attention layer focuses on the two inputs at the same time rather than separately.

Original: There is a single decoder, which receives the output of the attention layer and provides the ﬁnal translation.
Revised: There is a single decoder, which receives the output of the attention layer and provides the ﬁnal translation.

Original: Based on the paper Zoph & Knight (2016), the ﬁnal outputs of the encoders ( h t ) are concatenated and a linear transformation is applied to the concatenated output which is activated by tanh using
Revised: Based on the paper Zoph & Knight (2016), the ﬁnal outputs of the encoders ( h t ) are concatenated and a linear transformation is applied to the concatenated output which is activated by tanh using

Original: Equation 5.
Revised: Equation 5.

Original: On the other hand, the ﬁnal states ( c t ) are simply added as shown by Equation 6.
Revised: On the other hand, the ﬁnal states ( c t ) are simply added as shown by Equation 6.

Original: In the attention, the different context vectors are calculated separately and concatenated to produce the ﬁnal output of the attention vector based on the Luong attention mechanism(Luong et al., 2015) using Equation 7.
Revised: In the attention, the different context vectors are calculated separately and concatenated to produce the ﬁnal output of the attention vector based on the Luong attention mechanism(Luong et al., 2015) using Equation 7.

Original: [Equation]
Revised: [Equation]

Original:  E XPERIMENT S ETUP
Revised:  E XPERIMENT S ETUP

Original: We evaluate the proposed approach for the language pair English-Amharic using the same training parameters for both the basic NMT and the combinational system.
Revised: We evaluate the proposed approach for the language pair English-Amharic using the same training parameters for both the basic NMT and the combinational system.

Original: The encoder-decoder setup has 1024 LSTM cells or hidden units with 1024 word embedding and the data has been trained forepochs.
Revised: The encoder-decoder setup has 1024 LSTM cells or hidden units with 1024 word embedding and the data has been trained forepochs.

Original: 4. C ORPUS U SED
Revised: 4. C ORPUS U SED

Original: This research uses the new International Version Bible because both the Amharic and English versions are translated from the same Dead Sea scrolls.
Revised: This research uses the new International Version Bible because both the Amharic and English versions are translated from the same Dead Sea scrolls.

Original: This makes it more accurately parallel than other versions of the Bible translation.
Revised: This makes it more accurately parallel than other versions of the Bible translation.

Original: The whole New Testament of the Bible has been used as a corpus providing a total of 8603 phrases and sentences.
Revised: The whole New Testament of the Bible has been used as a corpus providing a total of 8603 phrases and sentences.

Original: We have used two books of the Bible, Paul’s letter to the Romans (Romans) and the
Revised: We have used two books of the Bible, Paul’s letter to the Romans (Romans) and the

Original: Gospel according to Mark (Mark), as a test set.
Revised: Gospel according to Mark (Mark), as a test set.

Original: Google translate has been used as the main agent of translation for the manually built bilingual dictionary used by the CBMT.
Revised: Google translate has been used as the main agent of translation for the manually built bilingual dictionary used by the CBMT.

Original: 77% of the total 6,793 vocabulary words have been translated using
Revised: 77% of the total 6,793 vocabulary words have been translated using

Original: Google.
Revised: Google.

Original: In addition to Google translate; we have done a manual translation for 1,426 vocabulary words in the book of Romans and other 150 vocabulary words using the Bible.
Revised: In addition to Google translate; we have done a manual translation for 1,426 vocabulary words in the book of Romans and other 150 vocabulary words using the Bible.

Original: Manual translation here refers to the translation of each word using every entry it has in the Bible by a human.
Revised: Manual translation here refers to the translation of each word using every entry it has in the Bible by a human.

Original: Figure 5 shows the outcome of such translation for the word Acknowledge .
Revised: Figure 5 shows the outcome of such translation for the word Acknowledge .

Original: Manual translation helps to address the variations in Amharic translated words caused by gender (female or male), plural form and the different persons (ﬁrst, second and third persons).
Revised: Manual translation helps to address the variations in Amharic translated words caused by gender (female or male), plural form and the different persons (ﬁrst, second and third persons).

Original: English words do also have different Amharic translations based on their context as shown in Figure 3.
Revised: English words do also have different Amharic translations based on their context as shown in Figure 3.

Original: Acknowledge has been translated into four main stem words .
Revised: Acknowledge has been translated into four main stem words .

Revised: [Figure]

Original: D ATASET
Revised: D ATASET

Original: We have fed the same dataset to all systems with minor variations.
Revised: We have fed the same dataset to all systems with minor variations.

Original: In the CBMT, we have used the book of Mark and the book of Romans as a test set.
Revised: In the CBMT, we have used the book of Mark and the book of Romans as a test set.

Original: The ﬂooded texts for the book of Romans were the book of Romans itself and Paulian epistles without Romans.
Revised: The ﬂooded texts for the book of Romans were the book of Romans itself and Paulian epistles without Romans.

Original: The ﬂooded texts for the book of
Revised: The ﬂooded texts for the book of

Original: Mark were the book of Mark itself and the gospels without Mark.
Revised: Mark were the book of Mark itself and the gospels without Mark.

Original: The books have been ﬂooded to themselves in order to evaluate the performance of the CBMT when the searched text is found in the ﬂooded text and also to see the impact of the bilingual dictionary on the CBMT.
Revised: The books have been ﬂooded to themselves in order to evaluate the performance of the CBMT when the searched text is found in the ﬂooded text and also to see the impact of the bilingual dictionary on the CBMT.

Original: The combinational system has two different test sets and different models.
Revised: The combinational system has two different test sets and different models.

Original: The ﬁrst test set has the output of the CBMT and the source sentence as an input for the NMT.
Revised: The ﬁrst test set has the output of the CBMT and the source sentence as an input for the NMT.

Original: The second test set gives the original target text and the source sentence as an input to the NMT models and we have called it the ideal approach.
Revised: The second test set gives the original target text and the source sentence as an input to the NMT models and we have called it the ideal approach.

Original: This was done so to see the impact of the CBMT output and the errors introduced by the CBMT on the combinational system.
Revised: This was done so to see the impact of the CBMT output and the errors introduced by the CBMT on the combinational system.

Original: In the basic NMT and combinational system, similar dataset as the CBMT is used.
Revised: In the basic NMT and combinational system, similar dataset as the CBMT is used.

Original: We have used
Revised: We have used

Original: Paulian epistle without Romans to train a basic NMT model and the combinational model.
Revised: Paulian epistle without Romans to train a basic NMT model and the combinational model.

Original: Then we have tested the model using the book of Romans as the test or holdout set.
Revised: Then we have tested the model using the book of Romans as the test or holdout set.

Original: We have used 10fold cross validation(Dietterich, 1998) to train and test the basic NMT model and the combinational model with Romans.
Revised: We have used 10fold cross validation(Dietterich, 1998) to train and test the basic NMT model and the combinational model with Romans.

Original: In a similar manner, we have used 10-fold cross validation(Dietterich, 1998) to train the basic NMT model and the combinational model with the book of Mark.
Revised: In a similar manner, we have used 10-fold cross validation(Dietterich, 1998) to train the basic NMT model and the combinational model with the book of Mark.

Original: We have also used holdout validation(Raschka, 2018) with a random 80% training and 20% test data split alongside the 10-fold cross validation for both Mark and Romans to obtain a more general representation of the results.
Revised: We have also used holdout validation(Raschka, 2018) with a random 80% training and 20% test data split alongside the 10-fold cross validation for both Mark and Romans to obtain a more general representation of the results.

Original:  EVALUATION METHOD
Revised:  EVALUATION METHOD

Original: We have measured the translation performance based on the fullness of the translation, weather the system translates all words; context awareness, whether the translation is true to the context and
Revised: We have measured the translation performance based on the fullness of the translation, whether the system translates all words; context awareness, whether the translation is true to the context and

Original: Coherence, whether the translated sentence has a smooth ﬂow of words in terms of syntax.
Revised: Coherence, whether the translated sentence has a smooth ﬂow of words in terms of syntax.

Original: In this research, the BLUE score is the chosen method of evaluation.
Revised: In this research, the BLEU score is the chosen method of evaluation.

Original: It answers all the aboveenlisted criteria.
Revised: It answers all the aboveenlisted criteria.

Original: The quality of translation is measured by the correspondence between a machine’s output and that of a human(Kishore et al., 2002).
Revised: The quality of translation is measured by the correspondence between a machine’s output and that of a human(Kishore et al., 2002).

Original: BLEU score is deﬁned in the range between 0 and 1 (or in percentage between 0 and 100) where 1 is a perfect match with the reference and 0 is for no words matched.
Revised: BLEU score is deﬁned in the range between 0 and 1 (or in percentage between 0 and 100) where 1 is a perfect match with the reference and 0 is for no words matched.

Original:  RESULTS AND DISCUSSION
Revised:  RESULTS AND DISCUSSION

Original: This section provides the results obtained along with a brief explanation of the factors and the components.
Revised: This section provides the results obtained along with a brief explanation of the factors and the components.

Original: 6. CBMT R ESULT AND D ISCUSSION
Revised: 6. CBMT R ESULT AND D ISCUSSION

Original: The system has been tested using a custom-made dictionary using Google translate and manual translation.
Revised: The system has been tested using a custom-made dictionary using Google translate and manual translation.

Original: We have generated the vocabulary of the dictionary from the English version of the NIV
Revised: We have generated the vocabulary of the dictionary from the English version of the NIV

Original: Bible.
Revised: Bible.

Original: Table 1 depicts the CBMT test results obtained, using BLUE score evaluation method
Revised: Table 1 depicts the CBMT test results obtained, using BLEU score evaluation method.

Original: [Table]
Revised: We

Original: [Table]
Revised: [Table]

Original: We have implemented manual translation for the book of Romans on about 80% of its total vocabulary.
Revised: have implemented manual translation for the book of Romans on about 80% of its total vocabulary.

Original: Hence, it has a better performance yield than the book of Mark, whose translation was solely dependent on Google translate.
Revised: Hence, it has a better performance yield than the book of Mark, whose translation was solely dependent on Google translate.

Original: This is so both when they are ﬂooded to the text that contained them (by 48%) and when they are ﬂooded to the text without them (by 6%).
Revised: This is so both when they are ﬂooded to the text that contained them (by 48%) and when they are ﬂooded to the text without them (by 6%).

Original: However, the translation of
Revised: However, the translation of

Original: Romans does not produce a 100% as would be expected when it is part of the ﬂooded document.
Revised: Romans does not produce a 100% as would be expected when it is part of the ﬂooded document.

Original: This is mainly because the system selects the overlapping N-gram based on the number of words matched, two consecutive phrases that may have a high overlap but which are not the correct ones may be selected.
Revised: This is mainly because the system selects the overlapping N-gram based on the number of words matched, two consecutive phrases that may have a high overlap but which are not the correct ones may be selected.

Original: 6. NMT R ESULT AND D ISCUSSION
Revised: 6. NMT R ESULT AND D ISCUSSION

Original: The NMT test results obtained using BLUE score evaluation method are depicted in Table 
Revised: The NMT test results obtained using BLEU score evaluation method are depicted in Table 2.

Original: [Table]
Revised: The

Original: [Table]
Revised: [Table]

Original: The test result obtained from Mark was better than that from Romans by an average of 1.62%.
Revised: test result obtained from Mark was better than that from Romans by an average of 1.62%.

Original: Although insigniﬁcant difference, it attributes to Marks’ writing having similar words unlike the diverse word selection in Romans(Clay, 2018)
Revised: Although insigniﬁcant difference, it attributes to Marks’ writing having similar words unlike the diverse word selection in Romans(Clay, 2018).

Original: . 6. CBMT AND NMT C OMBINATION R ESULT AND D ISCUSSION
Revised: 6. CBMT AND NMT C OMBINATION R ESULT AND D ISCUSSION

Original: There are two test cases for this section.
Revised: There are two test cases for this section.

Original: In the ﬁrst case, we have given the NMT the source sentence and the output of the CBMT as an input per the proposed methodology.
Revised: In the ﬁrst case, we have given the NMT the source sentence and the output of the CBMT as an input per the proposed methodology.

Original: Table 3 shows the results obtained from such a setup.[Table]
Revised: Table 3 shows the results obtained from such a setup.

Original: In the second case
Revised: In the second case, we have given the NMT the English source sentence

Original: [Table]
Revised: [Table]

Original: we have given the NMT the English source sentence and the original Amharic as an input creating an ideal system.
Revised: and the original Amharic as an input creating an ideal system.

Original: The test results obtained using BLUE score evaluation method, are depicted in Table 4 for the ideal combinational system [Table] [Table] [Table]
Revised: The test results obtained using BLEU score evaluation method, are depicted in Table 4 for the ideal combinational system.

Original: In the ﬁrst case
Revised: In the ﬁrst case, when the CBMT output is used as an input to the NMT, the Book of Romans performed better than

Original: [Table]
Revised: [Table]

Original: when the CBMT output is used as an input to the NMT

Original: [Table]
Revised: [Table]

Original: theBook of Romans performed better than the book of Mark by 1.71%.
Revised: the book of Mark by 1.71%.

Original: The CBMT output of Romans is better than that by the book of Mark and its impact has propagated to the combinational system.
Revised: The CBMT output of Romans is better than that by the book of Mark and its impact has propagated to the combinational system.

Original: In the ideal case scenario the results is more or less the same.
Revised: In the ideal case scenario the results is more or less the same.

Original: The result for the book of Romans was better than the book of Mark by only 0.63%
Revised: The result for the book of Romans was better than the book of Mark by only 0.63%

Original: [Table]
Revised: [Table]

Original: 6. D ISCUSSION OF ALL R ESULTS
Revised: 6. D ISCUSSION OF ALL R ESULTS

Original: The average of the results obtained from the systems have been calculated and shown in Tablesfor comparison.
Revised: The average of the results obtained from the systems have been calculated and shown in Tablesfor comparison.

Original: The ideal combinational system, which takes the original target
Revised: The ideal combinational system, which takes the original target

Original: Amharic text as the
Revised: Amharic text as the

Original: [Table]
Revised: [Table]

Original: second input, has performed better on average with 11.34 BLUE score gain over the NMT.
Revised: second input, has performed better on average with 11.34 BLEU score gain over the NMT.

Original: The ideal system, however, did not outperform the CBMT on average but produced results in the same range.
Revised: The ideal system, however, did not outperform the CBMT on average but produced results in the same range

Revised: [Table]

Original: The combinational system with CBMT output given as the second input for the NMT, achieves 2.
Revised: The combinational system with CBMT output given as the second input for the NMT, achieves 2.

Original: BLUE score point over the simple NMT.
Revised: BLEU score point over the simple NMT.

Original: The CBMT without being provided the target ﬂooded data has performed better by 14.23 BLUE points over the simple NMT.
Revised: The CBMT without being provided the target ﬂooded data has performed better by 14.23 BLEU points over the simple NMT [Table]

Original:  CONCLUSION
Revised:  CONCLUSION

Original: The research set out to ﬁnd a combinational system with components that complement each other when translating a document from English to Amharic.
Revised: The research set out to ﬁnd a combinational system with components that complement each other when translating a document from English to Amharic.

Original: We have proposed the CBMT and the NMT as the complementing pair in terms of parallel data size, accuracy, context awareness with translation and coherence.
Revised: We have proposed the CBMT and the NMT as the complementing pair in terms of parallel data size, accuracy, context awareness with translation and coherence.

Original: The CBMT system performed better than the basic NMT and the combinational system given the same size of data.
Revised: The CBMT system performed better than the basic NMT and the combinational system given the same size of data.

Original: However, the ideal combination of the CBMT and NMT has a BLUE score in thesame rangeas that of the CBMT while outperforming the simple NMT by 11.34 BLUE points.
Revised: However, the ideal combination of the CBMT and NMT has a BLEU score in the range of the CBMT while outperforming the simple NMT by 11.34 BLEU points.

Revised: For the book of Mark whose writing resembles the other Gospels, the ideal combination outperformed all.

Original: This entails that with smaller increase in parallel corpus  the ideal system will outperform both individual systems.
Revised: This entails that with smaller increase in parallel corpus for the NMT of the ideal system will outperform both individual systems.

Original: The output from the CBMT has a great impact on the performance of the combinational systems as seen by the performance of the proposed combinational system compared to the ideal system.
Revised: The output from the CBMT has a great impact on the performance of the combinational systems as seen by the performance of the proposed combinational system compared to the ideal system.

Original: The proposed combinational system still has outperformed the basic NMT by 2.805 BLUE score points in spite of the errors introduced by the CBMT.
Revised: The proposed combinational system still has outperformed the basic NMT by 2.805 BLEU score points in spite of the errors introduced by the CBMT.

Original: Therefore, awell-built CBMT with a well-built bilingual dictionary that produces a close to ideal output  makes a ﬂuent combinational system that outperforms a simple NMT and a basic CBMT system.
Revised: Therefore, a CBMT with a well-built bilingual dictionary that produces a close to ideal output along with a well-trained NMT with sufﬁcient data makes a ﬂuent combinational system that outperforms a simple NMT and a basic CBMT system.

Original: In conclusion, the study suggests the combinational system for translation of English to Amharic language.
Revised: In conclusion, the study suggests the combinational system for translation of English to Amharic language.