Original: O N
Revised: O N

Original: THE
Revised: THE

Original: USE
Revised: USE

Original: OF
Revised: OF

Original: LINGUISTIC
Revised: LINGUISTIC

Original: SIMILARITIES
Revised: SIMILARITIES

Original: TO
Revised: TO

Original: IMPROVE
Revised: IMPROVE

Original: N EURAL
Revised: N EURAL

Original: M ACHINE
Revised: M ACHINE

Original: T RANSLATION
Revised: T RANSLATION

Original: FOR
Revised: FOR

Original: A FRICAN L ANGUAGES
Revised: A FRICAN L ANGUAGES

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: In recent years, there has been a resurgence in research on empirical methods for machine translation.
Revised: In recent years, there has been a resurgence in research on empirical methods for machine translation.

Original: Most of this research has been focused on high-resource,
Revised: Most of this research has been focused on high-resource,

Original: European languages.
Revised: European languages.

Original: Despite the fact that around 30% of all languages spoken worldwide are African, the latter have been heavily under investigated and this, partly due to the lack of public parallel corpora online.
Revised: Despite the fact that around 30% of all languages spoken worldwide are African, the latter have been heavily under investigated and this, partly due to the lack of public parallel corpora online.

Original: Furthermore, despite their large number (more than 2,000) and the similarities between them, there is currently no publicly available study on how to use this multilingualism (and associated similarities) to improve machine translation systems performance on African languages.
Revised: Furthermore, despite their large number (more than 2,000) and the similarities between them, there is currently no publicly available study on how to use this multilingualism (and associated similarities) to improve machine translation systems performance on African languages.

Original: So as to address these issues, we propose a new dataset for African languages that provides parallel data for vernaculars not present in commonly used dataset like JW300.
Revised: So as to address these issues, we propose a new dataset for African languages that provides parallel data for vernaculars not present in commonly used dataset like JW300.

Original: To exploit multilingualism, we ﬁrst use a historical approach based on migrations of population to identify similar vernaculars.
Revised: To exploit multilingualism, we ﬁrst use a historical approach based on migrations of population to identify similar vernaculars.

Original: We also propose a new metric to automatically evaluate similarities between languages.
Revised: We also propose a new metric to automatically evaluate similarities between languages.

Original: This new metric does not require word level parallelism like traditional methods but only paragraph level parallelism.
Revised: This new metric does not require word level parallelism like traditional methods but only paragraph level parallelism.

Original: We then show that performing Masked Language
Revised: We then show that performing Masked Language

Original: Modelling and Translation Language Modeling in addition to multi-task learning on a cluster of similar languages leads to a strong boost of performance in translating individual pairs inside this cluster.
Revised: Modelling and Translation Language Modeling in addition to multi-task learning on a cluster of similar languages leads to a strong boost of performance in translating individual pairs inside this cluster.

Original: In particular, we record an improvement of 29 BLEU on the pair Baﬁa-Ewondo using our approaches compared to previous work methods that did not exploit multilingualism in any way.
Revised: In particular, we record an improvement of 29 BLEU on the pair Baﬁa-Ewondo using our approaches compared to previous work methods that did not exploit multilingualism in any way.

Original: Finally, we release the dataset and code of this work to ensure reproducibility and accelerate research in this domain.
Revised: Finally, we release the dataset and code of this work to ensure reproducibility and accelerate research in this domain.

Original:  I NTRODUCTION
Revised:  I NTRODUCTION

Original: Machine Translation (MT) of African languages is a challenging problem because of multiple reasons.
Revised: Machine Translation (MT) of African languages is a challenging problem because of multiple reasons.

Original: As pointed out by Martinus & Abbott (2019), the main ones are:
Revised: As pointed out by Martinus & Abbott (2019), the main ones are:

Original: • Morphological complexity and diversity of African languages : Africa is home to around 2144 languages out of nowadays 7111 living languages (they thus make 30.15% of all living languages) with often different alphabets (M. Eberhard et al.
Revised: • Morphological complexity and diversity of African languages : Africa is home to around 2144 languages out of nowadays 7111 living languages (they thus make 30.15% of all living languages) with often different alphabets (Eberhard et al.

Original: • Lack/Absence of large parallel datasets for most language pairs.
Revised: • Lack/Absence of large parallel datasets for most language pairs.

Original: • Discoverability:
Revised: • Discoverability:

Original: The existing resources for African languages are often hard to ﬁnd.
Revised: The existing resources for African languages are often hard to ﬁnd.

Original: • Reproducibility: Data and code of existing research are rarely shared, making it difﬁcult for other researchers to reproduce the results properly.
Revised: • Reproducibility: Data and code of existing research are rarely shared, making it difﬁcult for other researchers to reproduce the results properly.

Original: • Lack of benchmarks: Due to the low discoverability and the lack of research in the ﬁeld, there are no publicly available benchmark or leaderboard to compare new machine translation techniques to.
Revised: • Lack of benchmarks: Due to the low discoverability and the lack of research in the ﬁeld, there are no publicly available benchmark or leaderboard to compare new machine translation techniques to.

Original: However, despite the strong multilingualism of the continent, previous research focused solely on translating individual pairs of language without taking advantage of the potential similarities between all of them.
Revised: However, despite the strong multilingualism of the continent, previous research focused solely on translating individual pairs of language without taking advantage of the potential similarities between all of them.

Original: The main purpose of this work is to exploit this multilingualism, starting from a few languages spoken in Central and West Africa, to produce better machine translation systems.
Revised: The main purpose of this work is to exploit this multilingualism, starting from a few languages spoken in Central and West Africa, to produce better machine translation systems.

Original: Our contributions can be summarised as follow :
Revised: Our contributions can be summarised as follows :

Original: We provide new parallel corpora extracted from the Bible for several pairs of African vernacular that were not available until now as well as the code used to perform this extraction.
Revised: We provide new parallel corpora extracted from the Bible for several pairs of African vernacular that were not available until now as well as the code used to perform this extraction.

Original: We present a method for aggregating languages together based on their historical origins, their morphologies, their geographical and cultural distributions etc...
Revised: We present a method for aggregating languages together based on their historical origins, their morphologies, their geographical and cultural distributions etc...

Original: We also propose of a new metric to evaluate similarity between languages : this metric, based on language models, doesn’t require word level parallelism (contrary to traditional like (Levenshtein, 1965) and (Swadesh, 1952))
Revised: We also propose of a new metric to evaluate similarity between languages : this metric, based on language models, doesn’t require word level parallelism (contrary to traditional like (Levenshtein, 1965) and (Swadesh, 1952))

Original: but only paragraph level parallelism.
Revised: but only paragraph level parallelism.

Original: It also takes into account the lack of translation of words present in (Swadesh, 1952) but not in African vernaculars (like ”snow”).
Revised: It also takes into account the lack of translation of words present in (Swadesh, 1952) but not in African vernaculars (like ”snow”).

Original: Using the language clusters created using the previous similarities, we show that Translation Language Modelling ((Lample & Conneau, 2019)) and multi-task learning generally improve the performance on individual pairs inside these clusters.
Revised: Using the language clusters created using the previous similarities, we show that Translation Language Modelling ((Lample & Conneau, 2019)) and multi-task learning generally improve the performance on individual pairs inside these clusters.

Original: We make our data, code and benchmark publicly available.
Revised: We make our data, code and benchmark publicly available.

Original: The rest of the paper is organised as follow: In section 2, we discuss the motivation of this work i.e. problems faced by African communities that could be solved with machine translation systems.
Revised: The rest of the paper is organised as follows: In section 2, we discuss the motivation of this work i.e. problems faced by African communities that could be solved with machine translation systems.

Original: Related work is described in 3 and relevant background is outlined in 4.
Revised: Related work is described in 3 and relevant background is outlined in 4.

Original: In section 5, we describe our dataset and provide more details on the language aggregation methods.
Revised: In section 5, we describe our dataset and provide more details on the language aggregation methods.

Original: In section 6 we present the experimental protocol and the results of our methodology on machine translation of 3 African (Cameroonian) vernaculars.
Revised: In section 6 we present the experimental protocol and the results of our methodology on machine translation of 3 African (Cameroonian) vernaculars.

Original: Lastly, we summarise our work and conclude in 7.
Revised: Lastly, we summarise our work and conclude in 7.

Original:  M OTIVATION
Revised:  M OTIVATION

Original: Africa, because of its multilingual nature, faces a lot of communication problems.
Revised: Africa, because of its multilingual nature, faces a lot of communication problems.

Original: In particular, most
Revised: In particular, most

Original: African countries have colonial languages (French, English, Portuguese, etc...) as ofﬁcial languages (with sometimes one or two African dialects).
Revised: African countries have colonial languages (French, English, Portuguese, etc...) as ofﬁcial languages (with sometimes one or two African dialects).

Original: The latter are those taught in schools and used in administrations and workplaces (which are often located in urban areas).
Revised: The latter are those taught in schools and used in administrations and workplaces (which are often located in urban areas).

Original: In contrast, rural and remote areas citizens mainly speak African dialects (especially the older ones and the less educated ones).
Revised: In contrast, rural and remote areas citizens mainly speak African dialects (especially the older ones and the less educated ones).

Original: This situation creates the following fairness and ethical concerns (amongst others):
Revised: This situation creates the following fairness and ethical concerns (amongst others):

Original: • Remote area population often have difﬁculties communicating during medical consultations since they mainly speak the local languages, contrary to the doctors who mostly speak French, English, etc....
Revised: • Remote area population often have difﬁculties communicating during medical consultations since they mainly speak the local languages, contrary to the doctors who mostly speak French, English, etc....

Original: Similar situations arise when NGOs try to intervene in rural regions.
Revised: Similar situations arise when NGOs try to intervene in rural regions.

Original: • This discrepancy between spoken languages across different regions of individual African countries make the spread of misinformation easier (especially in rural regions and especially during election periods).
Revised: • This discrepancy between spoken languages across different regions of individual African countries make the spread of misinformation easier (especially in rural regions and especially during election periods).

Original: • Young Africans (in particular those living in urban areas) struggle to learn their vernaculars.
Revised: • Young Africans (in particular those living in urban areas) struggle to learn their vernaculars.

Original: Lack of documentation and schools dealing with African languages as well as the scarcity of translators and trainers make the situation even more complicated.
Revised: Lack of documentation and schools dealing with African languages as well as the scarcity of translators and trainers make the situation even more complicated.

Original: For all of the reasons above, it is essential to set up translation systems for these languages.
Revised: For all of the reasons above, it is essential to set up translation systems for these languages.

Original:  R ELATED WORK
Revised:  R ELATED WORK

Original:  Wilken et al.
Revised: Several scholarships have been conducted on machine translation for African languages, Wilken et al.

Original: (2012) performed statistical phrase-based translation for English to Setswana translation.
Revised: (2012) performed statistical phrase-based translation for English to Setswana translation.

Original: This research used linguistically-motivated pre and post processing of the corpus in order to improve the translations.
Revised: This research used linguistically-motivated pre and post processing of the corpus in order to improve the translations.

Original: The system was trained on the Autshumato dataset (Groenewald &
Revised: The system was trained on the Autshumato dataset (Groenewald &

Original: Fourie (2009)) and also used an additional monolingual dataset.
Revised: Fourie (2009)) and also used an additional monolingual dataset.

Original: They reached a BLEU score of 28.8.
Revised: They reached a BLEU score of 28.8.

Original: McKellar (2014) used statistical machine translation for English to Xitsonga translation.
Revised: McKellar (2014) used statistical machine translation for English to Xitsonga translation.

Original: The models were trained on the Autshumato data (Groenewald & Fourie (2009)), as well as a large monolingual corpus.
Revised: The models were trained on the Autshumato data (Groenewald & Fourie (2009)), as well as a large monolingual corpus.

Original: A factored machine translation system was used, making use of a combination of lemmas and part of speech tags.
Revised: A factored machine translation system was used, making use of a combination of lemmas and part of speech tags.

Original: It obtained a BLEU score of 37.31.
Revised: It obtained a BLEU score of 37.31.

Original: Niekerk (2014) used unsupervised word segmentation with phrase-based statistical machine translation models.
Revised: Niekerk (2014) used unsupervised word segmentation with phrase-based statistical machine translation models.

Original: These models translate from English to Afrikaans, N. Sotho, Xitsonga and isiZulu.
Revised: These models translate from English to Afrikaans, N. Sotho, Xitsonga and isiZulu.

Original: The parallel corpora were created by crawling online sources and ofﬁcial government data and aligning these sentences using the HunAlign software package.
Revised: The parallel corpora were created by crawling online sources and ofﬁcial government data and aligning these sentences using the HunAlign software package.

Original: Large monolingual datasets were also used.
Revised: Large monolingual datasets were also used.

Original: They reached a BLEU score of 71 on English to Afrikaans and 37.9 on English to N.Sotho.
Revised: They reached a BLEU score of 71 on English to Afrikaans and 37.9 on English to N.Sotho.

Original: Wolff & Kotze (2014) performed word translation for English to isiZulu.
Revised: Wolff & Kotze (2014) performed word translation for English to isiZulu.

Original: The translation system was trained on a combination of Autshumato, Bible, and data obtained from the South African Constitution.
Revised: The translation system was trained on a combination of Autshumato, Bible, and data obtained from the South African Constitution.

Original: All of the isiZulu text was syllabiﬁed prior to the training of the word translation system.
Revised: All of the isiZulu text was syllabiﬁed prior to the training of the word translation system.

Original: Abbott & Martinus (2018) trained Transformer models for English to Setswana on the parallel
Revised: Abbott & Martinus (2018) trained Transformer models for English to Setswana on the parallel

Original: Autshumato dataset (Groenewald & Fourie (2009)) and obtained a BLEU score of 33.53.
Revised: Autshumato dataset (Groenewald & Fourie (2009)) and obtained a BLEU score of 33.53.

Original: Data was not cleaned nor was any additional data used.
Revised: Data was not cleaned nor was any additional data used.

Original: This is the rare study reviewed that released datasets and code.
Revised: This is the rare study reviewed that released datasets and code.

Original: Martinus & Abbott (2019) used Google Translate to perform English to Afrikaans translation and achieved a BLEU score of 41.18 To the best of our knowledge, Masakhane (2020) are the only ones to have carried out research on a wide variety of African languages.
Revised: Martinus & Abbott (2019) used Google Translate to perform English to Afrikaans translation and achieved a BLEU score of 41.18 To the best of our knowledge, Masakhane (2020) are the only ones to have carried out research on a wide variety of African languages.

Original: They used the JW300 dataset  from opus collection 1 and trained Transformer models on it.
Revised: They used the JW300 dataset (Agi´c & Vuli´c, 2019) from opus collection 1 and trained Transformer models on it.

Original: However, they did not do any preliminary linguistic study of their languages of interest.
Revised: However, they did not do any preliminary linguistic study of their languages of interest.

Original: Lastly, Emezue & Dossou (2020) provided a benchmark for Fon to French translation using the same JW300 dataset.
Revised: Lastly, Emezue & Dossou (2020) provided a benchmark for Fon to French translation using the same JW300 dataset (Agi´c & Vuli´c, 2019).

Original: They used a GRU (Chung et al.
Revised: They used a GRU (Chung et al.

Original: sequence to sequence model with attention (Bahdanau et al.
Revised: sequence to sequence model with attention (Bahdanau et al.

Original: and obtained a BLEU score of 30.55.
Revised: and obtained a BLEU score of 30.55.

Original: These last two studies are ones of the very few to have released datasets and code.
Revised: These last two studies are ones of the very few to have released datasets and code.

Original:  (Johnson et al., 2016) applied multi-task learning to a single encoder-decoder LSTM model to translate between English, French and German.
Revised: Regarding the use of multilingualism in machine translation, (Johnson et al., 2016) applied multitask learning to a single encoder-decoder LSTM model to translate between English, French and German.

Original: The best gain of performance of their many-to-many model was 0.43 BLEU on (French,English) compared to a single-task model trained on that pair.
Revised: The best gain of performance of their many-to-many model was 0.43 BLEU on (French,English) compared to a single-task model trained on that pair.

Original: However, its worse decrease of performance was 2.75 BLEU on (English, German) (Ha et al., 2016) also worked on English, French and German.
Revised: However, its worse decrease of performance was 2.75 BLEU on (English, German) (Ha et al., 2016) also worked on English, French and German.

Original: Their best gain of performance compared to a single task model was 2.64 BLEU on (English, German) in an under-resourced scenario.
Revised: Their best gain of performance compared to a single task model was 2.64 BLEU on (English, German) in an under-resourced scenario.

Original: They used an original approach called mix-source where the pair (German,German) was added to the translation model training data.
Revised: They used an original approach called mix-source where the pair (German,German) was added to the translation model training data.

Original: The goal was to help the model obtaining a better representation of the source side and learn the translation relationships better.
Revised: The goal was to help the model obtaining a better representation of the source side and learn the translation relationships better.

Original: They argued that including monolingual data in this way might also improve the translation of some rare word types such as named entities.
Revised: They argued that including monolingual data in this way might also improve the translation of some rare word types such as named entities.

Original:  B ACKGROUND 4. N EURAL M ACHINE T RANSLATION (NMT)
Revised:  B ACKGROUND 4. N EURAL M ACHINE T RANSLATION (NMT)

Original: Machine Translation (MT) refers to fully automated software that can translate source content into target languages.
Revised: Machine Translation (MT) refers to fully automated software that can translate source content into target languages.

Original: Neural Machine Translation (NMT) is an approach to MT that uses an artiﬁcial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.
Revised: Neural Machine Translation (NMT) is an approach to MT that uses an artiﬁcial neural network to predict the likelihood of a sequence of words, typically modeling entire sentences in a single integrated model.

Original: NMT aims to translate an input sequence from a source language to a target language.
Revised: NMT aims to translate an input sequence from a source language to a target language.

Original: An NMT model usually consists of an encoder to map an input sequence to hidden representations, and a decoder to decode hidden representations to generate a sentence in the target language.
Revised: An NMT model usually consists of an encoder to map an input sequence to hidden representations, and a decoder to decode hidden representations to generate a sentence in the target language.

Original: NMThasmade rapid progress in recent years.
Revised: NMThasmade rapid progress in recent years.

Original: The attention module is ﬁrst introduced by Bahdanau et al.
Revised: The attention module is ﬁrst introduced by Bahdanau et al.

Original: (2015) , which is used to better align source words and target words.
Revised: (2015) , which is used to better align source words and target words.

Original: The encoder and decoder can be specialized as LSTM (Hochreiter & Schmidhuber (1997),Sutskever et al.
Revised: The encoder and decoder can be specialized as LSTM (Hochreiter & Schmidhuber (1997),Sutskever et al.

Original: (2014),Wu et al.
Revised: (2014),Wu et al.

Original: (2016)), CNN (Gehring et al., 2017) and Transformer (Vaswani et al., 2017).
Revised: (2016)), CNN (Gehring et al., 2017) and Transformer (Vaswani et al., 2017).

Original: A Transformer layer consists of three sublayers, a self-attention layer that processes sequential data taking the context of each timestep into consideration, an optional encoder-decoder attention layer that bridges the input sequence and target sequence which exists in decoder only, and a feed-forward layer for non-linear transformation.
Revised: A Transformer layer consists of three sublayers, a self-attention layer that processes sequential data taking the context of each timestep into consideration, an optional encoder-decoder attention layer that bridges the input sequence and target sequence which exists in decoder only, and a feed-forward layer for non-linear transformation.

Original: Transformer achieves the state-of-the-art results for NMT (Barrault et al., 2019).
Revised: Transformer achieves the state-of-the-art results for NMT (Barrault et al., 2019).

Original: 4. C ROSS 
Revised: 4. C ROSS - LINGUAL L ANGUAGE M ODEL (XLM) P RETRAINING

Original: Recently, pre-training techniques, like ELMo (Peters et al., 2018), GPT/GPT-2 (Radford et al., 2018), BERT (Devlin et al., 2019), cross-lingual language model (brieﬂy, XLM) (Lample & Conneau, 2019),
Revised: Recently, pre-training techniques, like ELMo (Peters et al., 2018), GPT/GPT-2 (Radford et al., 2018), BERT (Devlin et al., 2019), cross-lingual language model (brieﬂy, XLM) (Lample & Conneau, 2019),

Original: XLNet (Yang et al., 2019) and RoBERTa (Liu et al., 2019) have attracted more and more attention in machine learning and natural language processing communities.
Revised: XLNet (Yang et al., 2019) and RoBERTa (Liu et al., 2019) have attracted more and more attention in machine learning and natural language processing communities.

Original: The models are ﬁrst pre-trained on large amount of unlabeled data to capture rich representations of the input, and then applied to the downstream tasks by either providing context-aware embeddings of an input sequence (Peters et al., 2018), or initializing the parameters of the downstream model (Devlin et al., 2019) for ﬁne-tuning.
Revised: The models are ﬁrst pre-trained on large amount of unlabeled data to capture rich representations of the input, and then applied to the downstream tasks by either providing context-aware embeddings of an input sequence (Peters et al., 2018), or initializing the parameters of the downstream model (Devlin et al., 2019) for ﬁne-tuning.

Original: Such pre-training approaches lead to signiﬁcant improvements on natural language understanding tasks.
Revised: Such pre-training approaches lead to signiﬁcant improvements on natural language understanding tasks.

Original: A Cross-lingual language model (Lample & Conneau, 2019) is used to obtain a better initialization of sentence encoders for zero-shot cross-lingual classiﬁcation, a better initialization of supervised and unsupervised neural machine translation systems, a language models for low-resource languages and unsupervised cross-lingual word embeddings.
Revised: A Cross-lingual language model (Lample & Conneau, 2019) is used to obtain a better initialization of sentence encoders for zero-shot cross-lingual classiﬁcation, a better initialization of supervised and unsupervised neural machine translation systems, a language models for low-resource languages and unsupervised cross-lingual word embeddings.

Original: This is done through four objectives:
Revised: This is done through four objectives:

Original: • Shared sub-word vocabulary : XLM processes all languages with the same shared vocabulary created through Byte Pair Encoding (BPE) (Sennrich et al., 2015) • Causal Language Modeling (CLM) :
Revised: • Shared sub-word vocabulary : XLM processes all languages with the same shared vocabulary created through Byte Pair Encoding (BPE) (Sennrich et al., 2015) • Causal Language Modeling (CLM) :

Original: XLM causal language modeling (CLM) task consists of a Transformer language model trained to model the probability of a word given the previous words in a sentence P ( ω t | ω 1 , ..., ω t − 1 , θ ) .
Revised: XLM causal language modeling (CLM) task consists of a Transformer language model trained to model the probability of a word given the previous words in a sentence P ( ω t | ω 1 , ..., ω t − 1 , θ ) .

Original: • Masked Language Modeling (MLM) :
Revised: • Masked Language Modeling (MLM) :

Original: XLM consider the masked language modeling (MLM) objective of Devlin et al.
Revised: XLM consider the masked language modeling (MLM) objective of Devlin et al.

Original: (2019), also known as the Cloze task (Taylor, 1953).
Revised: (2019), also known as the Cloze task (Taylor, 1953).

Original: Following Devlin et al.
Revised: Following Devlin et al.

Original: XLM sample randomly 15% of the BPE tokens from the text streams, replace them by a [MASK] token 80% of the time, by a random token 10% of the time, andwe keep them unchanged 10% of the time.
Revised: XLM sample randomly 15% of the BPE tokens from the text streams, replace them by a [MASK] token 80% of the time, by a random token 10% of the time, and keep them unchanged 10% of the time.

Original: Differences between XLM approach and the MLM of Devlin et al.
Revised: Differences between XLM approach and the MLM of Devlin et al.

Original: (2019) include the use of text streams of an arbitrary number of sentences (truncated at 256 tokens) instead of pairs of sentences.
Revised: (2019) include the use of text streams of an arbitrary number of sentences (truncated at 256 tokens) instead of pairs of sentences.

Original: • Translation Language Modeling (TLM) :
Revised: • Translation Language Modeling (TLM) :

Original: Both the CLM and MLM objectives are un- supervised, only require monolingual data, and cannot be used to leverage parallel data when it is available.
Revised: Both the CLM and MLM objectives are unsupervised, only require monolingual data, and cannot be used to leverage parallel data when it is available.

Original: XLM introduce a new translation language modeling (TLM) objective for improving cross-lingual pretraining.
Revised: XLM introduce a new translation language modeling (TLM) objective for improving cross-lingual pretraining.

Original: TLM objective is an extension of MLM, where instead of considering monolingual text streams, we concatenate parallel sentences.
Revised: TLM objective is an extension of MLM, where instead of considering monolingual text streams, we concatenate parallel sentences.

Original: XLM randomly mask words in both the source and target sentences.
Revised: XLM randomly mask words in both the source and target sentences.

Original: To predict a word masked in an source sentence, the model can either attend to surrounding source words or to the target translation, encouraging the model to align the source and target representations.
Revised: To predict a word masked in an source sentence, the model can either attend to surrounding source words or to the target translation, encouraging the model to align the source and target representations.

Original: In particular, the model can leverage the target context if the source one is not sufﬁcient to infer the masked source words.
Revised: In particular, the model can leverage the target context if the source one is not sufﬁcient to infer the masked source words.

Original: XLM also reset the positions of target sentences to facilitate the alignment.
Revised: XLM also reset the positions of target sentences to facilitate the alignment.

Original: In this work, we used the XLM 2 framework to train and evaluate our models.
Revised: In this work, we used the XLM 2 framework to train and evaluate our models.

Original:  M ETHODOLOGY 5. D ATA
Revised:  M ETHODOLOGY 5. D ATA

Original: YouVersion provides biblical content in 1,477 languages 3 among which many (local) African ones.
Revised: YouVersion provides biblical content in 1,477 languages 3 among which many (local) African ones.

Original: We have extracted this content in the desired languages to build our dataset.
Revised: We have extracted this content in the desired languages to build our dataset.

Original: In particular, for some countries like Cameroon, YouVersion provide more languages (22 for the latter) than JW300  (18 for Cameroon) that is the most used data source for work on African languages (e.g Masakhane (2020))
Revised: In particular, for some countries like Cameroon, YouVersion provide more languages (22 for the latter) than JW300 (Agi´c & Vuli´c, 2019) (18 for Cameroon) that is the most used data source for work on African languages (e.g Masakhane (2020))

Original: We release our adaptable extraction script along with this publication.
Revised: We release our adaptable extraction script along with this publication.

Revised: The number of sentences per language is given in table 3.

Original: The Bible is one of the rare publicly available online corpora for low-resource languages such as those spoken in Africa.
Revised: The Bible is one of the rare publicly available online corpora for low-resource languages such as those spoken in Africa.

Original: Its structure (made of books, chapters and verse numbers) allows to automatically and unambiguously identify parallel paragraphs (verses) across all languages.
Revised: Its structure (made of books, chapters and verse numbers) allows to auto- matically and unambiguously identify parallel paragraphs (verses) across all languages.

Original: However, it suffers from several ﬂaws for machine translation:
Revised: However, it suffers from several ﬂaws for machine translation:

Original: In general, YouVersion only provides the New Testament for African languages, consisting of about 7958 verses only.
Revised: In general, YouVersion only provides the New Testament for African languages, consisting of about 7958 verses only.

Original: In the case of languages for which both New and Old Testament are available, one can retrieve around 31170 verses.2 https:
Revised: In the case of languages for which both New and Old Testament are available, one can retrieve around 31170 verses.

Original: https:/ .bible com/en/languages The Bible contains a lot of redundant sentences (”In truth I tell you” for example), verses made up of characters and places names etc... Making the translation task easier than if a closer to real world dataset like WMT was used.
Revised: The Bible contains a lot of redundant sentences (”In truth I tell you” for example), verses made up of characters and places names etc... Making the translation task easier than if a closer to real world dataset like WMT was used.

Original: Its antiquated writing style is also far from contemporary style.
Revised: Its antiquated writing style is also far from contemporary style.

Original: The alignment information is limited to verses rather than sentences unlike the JRC-Acquis corpus (Steinberger et al.
Revised: The alignment information is limited to verses rather than sentences unlike the JRC-Acquis corpus (Steinberger et al.

Original: for instance.
Revised: for instance.

Original: (Christodouloupoulos & Steedman, 2015) gives more details on pros and cons of using the Bible as a machine translation corpus.
Revised: (Christodouloupoulos & Steedman, 2015) gives more details on pros and cons of using the Bible as a machine translation corpus.

Original: Following (Lample & Conneau, 2019), we process all the languages with the same shared vocabulary created using Byte Pair Encoding (BPE) (Sennrich et al., 2015).
Revised: Following (Lample & Conneau, 2019), we process all the languages with the same shared vocabulary created using Byte Pair Encoding (BPE) (Sennrich et al., 2015).

Original: Due to the lack of speciﬁc tokenizers for African languages, we used an english one on all of them.
Revised: Due to the lack of speciﬁc tokenizers for African languages, we used an english one on all of them.

Original: 5. L ANGUAGES SIMILARITIES AND AGGREGATIONS 5.2. H ISTORICAL APPROACH FOR LANGUAGES AGGREGATION
Revised: 5. L ANGUAGES SIMILARITIES AND AGGREGATIONS 5.2. H ISTORICAL APPROACH FOR LANGUAGES AGGREGATION

Original: From a historical point of view, just like African borders, African languages have undergone the movements of the early inhabitants of the continent.
Revised: From a historical point of view, just like African borders, African languages have undergone the movements of the early inhabitants of the continent.

Original: These migrations have shaped and modiﬁed the languages spoken in multiple regions and led to resemblances between some of them.
Revised: These migrations have shaped and modiﬁed the languages spoken in multiple regions and led to resemblances between some of them.

Original: For these reasons, we examined and used languages aggreations proposed byM. Eberhard et al.
Revised: For these reasons, we examined and used languages aggreations proposed by Eberhard et al.

Original: (2020) that are based on their origins and evolutions.
Revised: (2020) that are based on their origins and evolutions.

Original: The intersection between our dataset andM. Eberhard et al.
Revised: The intersection between our dataset and Eberhard et al.

Original: (2020) analysis led to the following languages clusters:
Revised: (2020) analysis led to the following languages clusters:

Original: • Cluster 1 : (Baﬁa, Ewondo, Bulu) .
Revised: • Cluster 1 : (Baﬁa, Ewondo, Bulu) .

Original: These languages are all part of the Niger-Congo, Northwest 4 Narrow Bantu family are spoken in the central and southern regions of Cameroon.
Revised: These languages are all part of the Niger-Congo, Northwest 4 Narrow Bantu family and spoken in the central and southern regions of Cameroon.

Original: • Cluster 2 : (Ngiemboon, Ghomala, Limbum) .
Revised: • Cluster 2 : (Ngiemboon, Ghomala, Limbum) .

Original: They are part of the Niger-Congo, MbamNkam 5 family are spoken in the West Cameroon.
Revised: They are part of the Niger-Congo, MbamNkam 5 family and spoken in the West Cameroon.

Original: • Cluster 3 : (Dii, Doyago, Samba Leko, Peere) .
Revised: • Cluster 3 : (Dii, Doyago, Samba Leko, Peere) .

Original: These languages are spoken in the Northern region of Cameroon and belong to the Niger-Congo, Adamawa 6 family • Cluster 4 : (Guiziga, Mofa, Mofu-Gudur, Kapsiki, Guidar) .
Revised: These languages are spoken in the Northern region of Cameroon and belong to the Niger-Congo, Adamawa 6 family • Cluster 4 : (Guiziga, Mofa, Mofu-Gudur, Kapsiki, Guidar) .

Original: They are also spoken in Northern Cameroon but have been classﬁed as Afro-Asiatic languages.
Revised: They are also spoken in Northern Cameroon but have been classﬁed as Afro-Asiatic languages.

Original: In particular, they belong to the Biu-Mandara 7 family.
Revised: In particular, they belong to the Biu-Mandara 7 family.

Original: In the rest of this work we used the ﬁrst cluster mentioned above for the experiments : it is the smallest of the 4 and is made of some of the most spoken languages in Cameroon (compared to the others listed above)
Revised: In the rest of this work we used the ﬁrst cluster mentioned above for the experiments : it is the smallest of the 4 and is made of some of the most spoken languages in Cameroon (compared to the others listed above)

Original: 5.2. L ANGUAGE MODEL BASED APPROACH FOR LINGUSTIC SIMILARITIES
Revised: 5.2. L ANGUAGE MODEL BASED APPROACH FOR LINGUSTIC SIMILARITIES

Original: Despite providing a historically grounded methodology to identify languages families, methods such asM. Eberhard et al.
Revised: Despite providing a historically grounded methodology to identify languages families, methods such as Eberhard et al.

Original: (2020) are time-consuming and require a lot of background knowledge on speciﬁc population migration throughout history.
Revised: (2020) are time-consuming and require a lot of background knowledge on speciﬁc population migration throughout history.

Original: Such in-depth knowledge might not be available for all languages.
Revised: Such in-depth knowledge might not be available for all languages.

Original: Besides, they don’t provide a quantitative measure for languages similarity, making it difﬁcult for one to assess if a language L i is closer to a language L j than to a language L k .
Revised: Besides, they don’t provide a quantitative measure for languages similarity, making it difﬁcult for one to assess if a language L i is closer to a language L j than to a language L k .

Original: Quantitative metrics are necessary for well known clustering algorithms like K-Means or Hierarchical
Revised: Quantitative metrics are necessary for well known clustering algorithms like K-Means or Hierarchical

Original: Clustering.
Revised: Clustering.

Original: Multiple linguistic similarity metrics have been proposed in the past.
Revised: Multiple linguistic similarity metrics have been proposed in the past.

Original: The most popular are the ones used in lexicostatistics among which Swadesh lists (Swadesh, 1952) and Levenshtein distance (Levenshtein, 1965).
Revised: The most popular are the ones used in lexicostatistics among which Swadesh lists (Swadesh, 1952) and Levenshtein distance (Levenshtein, 1965).

Original: Nonetheless, the latter require data parallelism at word level and dictionaries might not be freely available online for all languages pairs.
Revised: Nonetheless, the latter require data parallelism at word level and dictionaries might not be freely available online for all languages pairs.

Original: Additionally, Swadesh lists are based on a small number of words that might not even exist in all languages.
Revised: Additionally, Swadesh lists are based on a small number of words that might not even exist in all languages.

Original: For example, the word ”snow” (present in the Swadesh 207 list) doesn’t have a translation in Baﬁa (likely because it never snows in this region).
Revised: For example, the word ”snow” (present in the Swadesh 207 list) doesn’t have a translation in Baﬁa (likely because it never snows in this region).

Original: To alleviate these problems, we propose a language model based metric for linguistic similarity that requires paragraph level parallelism only.
Revised: To alleviate these problems, we propose a language model based metric for linguistic similarity that requires paragraph level parallelism only.

Original: This approach formalises (using language models) the following human intuition : a child whose native language is L 1 may think that a language L 2 is similar to L 1 if words (possibly with the same meaning) in L 2 ”sounds”/”looks” like words in L
Revised: This approach formalises (using language models) the following human intuition : a child whose native language is L 1 may think that a language L 2 is similar to L 1 if words (possibly with the same meaning) in L 2 ”sounds”/”looks” like words in L

Original: For example, the english word ”bilingual” is translated in french by ”bilingue” and in spanish by ”biling¨ue” but its polish translation is ”dwujezyczny”.
Revised: For example, the english word ”bilingual” is translated in french by ”bilingue” and in spanish by ”biling¨ue” but its polish translation is ”dwujezyczny”.

Original: A french speaker can easily think that the spanish ”biling¨ue” is a french word both at the spelling level because of common syllables (”bi”, ”lin” and ”gu”/”g¨u”) and at the pronunciation level.
Revised: A french speaker can easily think that the spanish ”biling ¨ ue” is a french word both at the spelling level because of common syllables (”bi”, ”lin” and ”gu”/”g ¨ u”) and at the pronunciation level.

Original: Nevertheless, it is unlikely for the polish ”dwujezyczny” to be seen as a french word.
Revised: Nevertheless, it is unlikely for the polish ”dwujezyczny” to be seen as a french word.

Original: Following the success of recent language models like GPT-2 Radford et al.
Revised: Following the success of recent language models like GPT-2 Radford et al.

Original: (2019), the previous french speaker can be seen as a very powerful french language model lm fr .
Revised: (2019), the previous french speaker can be seen as a very powerful french language model lm fr .

Original: If we evaluate it on both ”biling¨ue” and ”dwujezyczny”, it should give a higher probabilty to the spanish ”biling¨ue” than to the polish ”dwujezyczny” because of the french ”bilingue”.
Revised: If we evaluate it on both ”biling ¨ ue” and ”dwujezyczny”, it should give a higher probabilty to the spanish ”biling ¨ ue” than to the polish ”dwujezyczny” because of the french ”bilingue”.

Original: More formally, we expect the BPE encoding to capture syllables like ”bi”,”lin” and ”gu”/”g ¨ u” 8 so that if
Revised: More formally, we expect the BPE encoding to capture syllables like ”bi”,”lin” and ”gu”/”g ¨ u” 8 so that if

Original: P lm fr ( bilingue ) =
Revised: P lm fr ( bilingue ) =

Original: P lm fr ( bi ) ∗ P
Revised: P lm fr ( bi ) ∗ P

Original: lm fr ( lin | bi ) ∗ P
Revised: lm fr ( lin | bi ) ∗ P

Original: lm fr ( gu | bi, lin ) ∗ P
Revised: lm fr ( gu | bi, lin ) ∗ P

Original: lm fr ( e | bi, lin, gu ) is relatively high then P
Revised: lm fr ( e | bi, lin, gu ) is relatively high then P

Original: lm fr ( biling ¨ ue ) =
Revised: lm fr ( biling ¨ ue ) =

Original: P lm fr ( bi ) ∗ P
Revised: P lm fr ( bi ) ∗ P

Original: lm fr ( lin | bi ) ∗ P
Revised: lm fr ( lin | bi ) ∗ P

Original: lm fr ( g ¨ u | bi, lin ) ∗ P
Revised: lm fr ( g ¨ u | bi, lin ) ∗ P

Original: lm fr ( e | bi, lin, g ¨ u ) should be greater than P lm fr ( dwujezyczny )
Revised: lm fr ( e | bi, lin, g ¨ u ) should be greater than P lm fr ( dwujezyczny )

Original: At a paragraph level, we expect that given two parallel paragraphs C spanish and C polish , lm fr gives a higher probability to C spanish i.e. P lm fr ( C spanish ) >
Revised: At a paragraph level, we expect that given two parallel paragraphs C spanish and C polish , lm fr gives a higher probability to C spanish i.e. P lm fr ( C spanish ) >

Original: P lm fr ( C polish )
Revised: P lm fr ( C polish )

Original: Given a trained language model LM 0 in language L 0 and two parallel corpora C 0 in L 0 and C 1 in language L 1 , we deﬁne the LM-similarity between L 0 and L 1 according to LM 0 and C 0 by
Revised: Given a trained language model LM 0 in language L 0 and two parallel corpora C 0 in L 0 and C 1 in language L 1 , we deﬁne the LM-similarity between L 0 and L 1 according to LM 0 and C 0 by

Original: [Equation]
Revised: [Equation]

Original: Where PPL LM 

Revised: Where PPL LM

Original: ( C i ) is the perplexity of the language model LM 0 on the corpus C i .
Revised: ( C i ) is the perplexity of the language model LM 0 on the corpus C i .

Original: It is important to precise that Accuracy (percentage of tokens correctly predicted by the language model) can be used in lieu of Perplexity (this may lead to different similarities between languages).
Revised: It is important to precise that Accuracy (percentage of tokens correctly predicted by the language model) can be used in lieu of Perplexity (this may lead to different similarities between languages).

Original: Despite the fact that this metric is not symmetric, it can be used to build clusters using a hierarchical clustering approach.
Revised: Despite the fact that this metric is not symmetric, it can be used to build clusters using a hierarchical clustering approach.

Original: We start by a set of 2 languages G= { L 1 , L 2 } , the language L ∗ that will be added to G 1 can be determined using the following formula (inspired by the unweighted average linkage clustering criteria) :[Equation] L
Revised: We start by a set of 2 languages G= { L 1 , L 2 } , the language L ∗ that will be added to G 1 can be determined using the following formula (inspired by the unweighted average linkage clustering criteria) :

Original: [Equation]
Revised: [Equation]

Original: In other words, L ∗ is the language in the set of possible languages L (but we exclude G 1 =
Revised: In other words, L ∗ is the language in the set of possible languages L (but we exclude G 1 =

Original: { L 1 , L 2 } ) that minimises the average ”distance” (LM-similarity) from L 1 and L 2 .
Revised: { L 1 , L 2 } ) that minimises the average ”distance” (LM-similarity) from L 1 and L 2 .

Original: By recursively applying this formula, one can build a cluster of any size.
Revised: By recursively applying this formula, one can build a cluster of any size.

Original:  E XPERIMENTS AND R ESULTS
Revised:  E XPERIMENTS AND R ESULTS

Original: For each ordered language pair in the cluster of interest (Baﬁa, Ewondo, Bulu) , we examine the machine translation performance (using BLEU score) on that pair if we add a third language to the pair fo form a cluster.
Revised: For each ordered language pair in the cluster of interest (Baﬁa, Ewondo, Bulu) , we examine the machine translation performance (using BLEU score) on that pair if we add a third language to the pair fo form a cluster.

Original: The model will thus become a multi-task machine translation model capable of translating 3 languages rather than 2.
Revised: The model will thus become a multi-task machine translation model capable of translating 3 languages rather than 2.

Original: The language (among the 22 in our dataset) to add is chosen in 3 ways in our work:
Revised: The language (among the 22 in our dataset) to add is chosen in 3 ways in our work:

Original: Using a historical approach likeM. Eberhard et al.
Revised: Using a historical approach like Eberhard et al.

Original: For the pair (Baﬁa,Ewondo) we will add Bulu .
Revised: For the pair (Baﬁa,Ewondo) we will add Bulu .

Original: It corresponds to the column Historical in Table2.
Revised: It corresponds to the column Historical in Table2.

Original: Using the LM-similarity (as described in 5.2.2).
Revised: Using the LM-similarity (as described in 5.2.2).

Original: For the pair (Bulu,Ewondo) , it will be Mofa .
Revised: For the pair (Bulu,Ewondo) , it will be Mofa .

Original: It corresponds to the column LM in Table
Revised: It corresponds to the column LM in Table3.

Original: Purely at random

Original: : it

Original: will serve

Original: as

Original: a baseline

Original:  to make sure that the difference in performance is due to the particular choices 
Revised: Purely at random : it will serve as a baseline to make sure that the difference in performance is due to the particular choices of our methodology rather than multi-task learning solely.

Original: of our

Original: methodology

Original: rather than multi-task learning

Original: solely

Original: It corresponds to the column Random Table
Revised: It corresponds to the column Random Table

Original: Having identiﬁed our clusters and to further exploit multilingualism, we follow the approach of
Revised: Having identiﬁed our clusters and to further exploit multilingualism, we follow the approach of

Original: Lample & Conneau (2019) by pre-training the model’s encoder with MLM and TLM objectives on the cluster languages, then ﬁne-tune the model on these same languages.
Revised: Lample & Conneau (2019) by pre-training the model’s encoder with MLM and TLM objectives on the cluster languages, then ﬁne-tune the model on these same languages.

Original: We use the average BLEU score on the validation sets across all the languages as stopping criterion.
Revised: We use the average BLEU score on the validation sets across all the languages as stopping criterion.

Original: Due to the multi-task nature of the model, this criterion is preferred to validation BLEU score on the pair of interest : in some cases, the model can ﬁrst have low, stagnating performance on that pair for a large number of epochs (greater than the patience, threshold to stop the training) but at the same time have good performance on the other pairs (leading to an increase of average BLEU score).
Revised: Due to the multi-task nature of the model, this criterion is preferred to validation BLEU score on the pair of interest : in some cases, the model can ﬁrst have low, stagnating performance on that pair for a large number of epochs (greater than the patience, threshold to stop the training) but at the same time have good performance on the other pairs (leading to an increase of average BLEU score).

Original: At the end of a training with average BLEU score as stopping criterion, the pair of interest ”catches up” with the other pairs and obtain high scores.
Revised: At the end of a training with average BLEU score as stopping criterion, the pair of interest ”catches up” with the other pairs and obtain high scores.

Original: We also compare the performance with model trained: 1) On the pair of interest without any form of MLM/TLM pre-training (Column None in Table 1).
Revised: We also compare the performance with model trained: 1) On the pair of interest without any form of MLM/TLM pre-training (Column None in Table 1).

Original: On the pair of interest with MLM+TLM pre-training (Column Pair in Table 1)
Revised: On the pair of interest with MLM+TLM pre-training (Column Pair in Table 1)

Original: 6. T RAINING DETAILS
Revised: 6. T RAINING DETAILS

Original: We used the default settings mentioned in the documentation of the XLM framework :
Revised: We used the default settings mentioned in the documentation of the XLM framework :

Original: Transformer architectures with 1024 hidden units, 8 heads, GELU activations (Hendrycks & Gimpel, 2016),
Revised: Transformer architectures with 1024 hidden units, 8 heads, GELU activations (Hendrycks & Gimpel, 2016),

Original: Dropout rate of 0.1 and learned positional embeddings, Adam optimizer (Kingma & Ba., 2014), a linear warmup (Vaswani et al., 2017) and learning rates of 0.0001 for both XLM pre-training and machine translation.
Revised: Dropout rate of 0.1 and learned positional embeddings, Adam optimizer (Kingma & Ba., 2014), a linear warmup (Vaswani et al., 2017) and learning rates of 0.0001 for both XLM pre-training and machine translation.

Original: For the MLM and TLM pre-training, we used the averaged perplexity over all the languages as a measure of performance and stopping criterion for training, and for machine translation, we used the average BLEU score across all the languages pairs of the models.
Revised: For the MLM and TLM pre-training, we used the averaged perplexity over all the languages as a measure of performance and stopping criterion for training, and for machine translation, we used the average BLEU score across all the languages pairs of the models.

Original: The patience was set to 20 for all models.
Revised: The patience was set to 20 for all models.

Original: The models were trained on one 48GB NVIDIA Quadro RTX 8000 GPU for Random , Historical and LM and on a 11GB NVIDIA RTX 

Revised: The models were trained on one 48GB NVIDIA Quadro RTX 8000 GPU for Random , Historical and LM and on a 11GB NVIDIA RTX

Original: Ti for None and Pair .
Revised: Ti for None and Pair .

Original: In all our experiments, we used 80% of the dataset as training data, 10% as validation data and 10% as test data.
Revised: In all our experiments, we used 80% of the dataset as training data, 10% as validation data and 10% as test data.

Original: 6. R ESULTS AND DISCUSSION
Revised: 6. R ESULTS AND DISCUSSION

Original: Table 1 describes the results of the baselines mentioned in 6 and our models.
Revised: Table 1 describes the results of the baselines mentioned in 6 and our models.

Original: We notice that using our historical approach systematically leads to a strong boost of performance and always outperforms the None , Pair and Random baselines.
Revised: We notice that using our historical approach systematically leads to a strong boost of performance and always outperforms the None , Pair and Random baselines.

Original: As the aggregation approach that led to the best results, we evaluated its impact on translating African vernaculars to European languages.
Revised: As the aggregation approach that led to the best results, we evaluated its impact on translating African vernaculars to European languages.

Original: We chose french as it is the most spoken language in Cameroon resulting in the cluster (Baﬁa, Bulu, Ewondo, French) on which MLM + TLM pretraining and multi-task learning were done and compared to None and
Revised: We chose french as it is the most spoken language in Cameroon resulting in the cluster (Baﬁa, Bulu, Ewondo, French) on which MLM + TLM pretraining and multi-task learning were done and compared to None and

Original: Pair baselines.
Revised: Pair baselines.

Original: Again, the historical approach outperformed all the baselines with notably a gain of 22 BLEU on the pair French-Ewondo .
Revised: Again, the historical approach outperformed all the baselines with notably a gain of 22 BLEU on the pair French-Ewondo .

Original: Our LM-similarity approach systematically performs better than the None and Pair baselines, suggesting that adding a language using this metric and do multi-task learning on the resulting cluster is a good heuristic even if one only cares about a speciﬁc pair of languages.
Revised: Our LM-similarity approach systematically performs better than the None and Pair baselines, suggesting that adding a language using this metric and do multi-task learning on the resulting cluster is a good heuristic even if one only cares about a speciﬁc pair of languages.

Original: It outperforms the baseline Random for 4 out of the 6 pairs of languages.
Revised: It outperforms the baseline Random for 4 out of the 6 pairs of languages.

Original: According to this similarity, the closest language to the pair ( Baﬁa,Ewondo ) is Bulu , thus reconstituting the historical cluster identiﬁed by 5.2.
Revised: According to this similarity, the closest language to the pair ( Baﬁa,Ewondo ) is Bulu , thus reconstituting the historical cluster identiﬁed by 5.2.

Original: without any human supervision.
Revised: without any human supervision.

Original: Randomly choosing this particular language would have happened with a 120 = 5% probability ( 20 = 22 − 2 , there are 22 languages in our dataset and we excludelanguages, Baﬁa and Ewondo , since we want to add another language to this pair).
Revised: Randomly choosing this particular language would have happened with a 120 = 5% probability ( 20 = 22 − 2 , there are 22 languages in our dataset and we excludelanguages, Baﬁa and Ewondo , since we want to add another language to this pair).

Original: The fact that the Random baseline performs worse than models derived from our methodologytimes out of 12 suggests that the performance boost is not solely due to the multi-task nature of the model but mostly because of the good choices of languages to add.
Revised: The fact that the Random baseline performs worse than models derived from our methodologytimes out of 12 suggests that the performance boost is not solely due to the multi-task nature of the model but mostly because of the good choices of languages to add.

Original: It is worth noticing that despite the MLM and TLM pre-training, the Random baseline performs worse than the baseline Pair 4 times out of 6.
Revised: It is worth noticing that despite the MLM and TLM pre-training, the Random baseline performs worse than the baseline Pair 4 times out of 6.

Original: This suggests that randomly adding languages may often hurt the performance compared to a simple model trained on the pair of interest.
Revised: This suggests that randomly adding languages may often hurt the performance compared to a simple model trained on the pair of interest

Original: [Table]
Revised: [Table]

Revised: column None is a baseline : it represents the BLEU score of a model trained on the pair without any MLM or TLM pre-training.

Original:  the language added was chosen using the LM similarity ad described in 5.2.
Revised: The column Pair is a baseline : it represents the BLEU score of a model trained on the pair with MLM and TLM pre-training. The column Random is also a baseline : it is the BLEU score of a 3 languages multi-task model where the language added was chosen purely at random. The column Historical refers to the BLEU score of our 3 languages multi-task model where the language added was chosen using clusters identiﬁed in 5.2.1. The column LM describes the BLEU score of our 3 languages, multi-task model where the language added was chosen using the LM similarity ad described in 5.2.

Original: We hypothesize that a multi-task machine translation model (potentially with MLM and TLM pretraining) is capable of exploiting similarities between similar languages.
Revised: We hypothesize that a multi-task machine translation model (potentially with MLM and TLM pretraining) is capable of exploiting similarities between similar languages.

Original: A similar phenomenon can be observed with human beings : it is easier for someone that speaks French and Spanish to learn
Revised: A similar phenomenon can be observed with human beings : it is easier for someone that speaks French and Spanish to learn

Original: Italian (since they are all Latin languages) rather than polish (that is a Lechitic language).
Revised: Italian (since they are all Latin languages) rather than polish (that is a Lechitic language).

Original:  C ONCLUSION
Revised:  C ONCLUSION

Original: In this work, we studied the impact of using multilingualism to improve machine translation of
Revised: In this work, we studied the impact of using multilingualism to improve machine translation of

Original: African languages.
Revised: African languages.

Original: Our ﬁrst contribution is the creation and public release of a new dataset (as well as the code used to create it) that increases the number African languages for which parallel machine translation corpora is available.
Revised: Our ﬁrst contribution is the creation and public release of a new dataset (as well as the code used to create it) that increases the number African languages for which parallel machine translation corpora is available.

Original: This study focused on pairs derived from a cluster of 3 of the most spoken languages in Cameroon.
Revised: This study focused on pairs derived from a cluster of 3 of the most spoken languages in Cameroon.

Original: Our second contribution is the proposal of a new metric to evaluate similarity between languages : this metric, based on language models, doesn’t require word level parallelism (contrary to traditional like (Levenshtein, 1965) and (Swadesh, 1952)) but only paragraph level parallelism.
Revised: Our second contribution is the proposal of a new metric to evaluate similarity between languages : this metric, based on language models, doesn’t require word level parallelism (contrary to traditional like (Levenshtein, 1965) and (Swadesh, 1952)) but only paragraph level parallelism.

Original: It also takes into account the lack of translation of words present in (Swadesh, 1952) but not in African languages (like ”snow”).
Revised: It also takes into account the lack of translation of words present in (Swadesh, 1952) but not in African languages (like ”snow”).

Original: Translation Language Modeling and multi-task learning were used to exploit multilingualism.
Revised: Translation Language Modeling and multi-task learning were used to exploit multilingualism.

Original: Our third contribution is the set of empirical evidences that doing multi-task learning on multiple similar languages generally improves the performances on individual pairs.
Revised: Our third contribution is the set of empirical evidences that doing multi-task learning on multiple similar languages generally improves the performances on individual pairs.

Original: The choice of the similarity is crucial since in our work, 4 times out of 6 a random choice leads to a decrease of performance.
Revised: The choice of the similarity is crucial since in our work, 4 times out of 6 a random choice leads to a decrease of performance.

Original: On the other hand, using our historical approach (derived fromM. Eberhard et al.
Revised: On the other hand, using our historical approach (derived from Eberhard et al.

Original: (2020)) systematically led to a boost of performance on this dataset and our LM similarity approach improved them 4 times out of 6.
Revised: (2020)) systematically led to a boost of performance on this dataset and our LM similarity approach improved them 4 times out of 6.