ANCIENT VOICES, MODERN TECHNOLOGY

Ancient Voices, Modern Technology: Low-Resource Neural MachineTranslation for Coptic Texts
Maxim Enis
Andrew Megalaa

ME4@WILLIAMS.EDU
ANM4@WILLIAMS.EDU

Williams College Computer Science

1. Introduction
Google Translate includes language translation models for a wide variety of language pairs, however
many of the lesser-spoken languages in the world are not included. Some initiatives, such as Meta AI’s
No Language Left Behind program (NLLB) (Team et al., 2022), aim to address this concern. Evenso, among the more than 31000 historically spoken languages, only a vanishingly small number havebeen successfully incorporated into translation models (Armstrong).
Coptic is a late-stage development of the Egyptian language that was primarily spoken in the period c. 325 – c. 800 AD (Layton, 2007). It is classified as extinct by the UNESCO Atlas of theWorld’s Languages in Danger (UNESCO, 2010) and has no native speakers. However, many historical Egyptian texts are written in Coptic, and linguists and historians are interested in deciphering thesetexts. The Coptic Scriptorium is one such research program, with ongoing efforts to translate all texts
available within the website (Schroeder and Zeldes, 2013-2023). However, due to the large quantityof texts and lack of qualified translators, many Coptic texts remain untranslated to this day.
In the present day, Coptic is primarily used in religious contexts within the Coptic Orthodox
Church. However, there is community interest in reviving the language for everyday use. Despite
past efforts that included developing resources on its vocabulary, grammar, and establishing teaching
institutions, these initiatives did not lead to widespread adoption within the community (Atiya, 1991).
The limited availability of online resources for learning Coptic and the absence of contemporary texts
in the language pose significant challenge for future revival efforts, hindering its appeal to new learners.
To address these challenges, we created, to our knowledge, the first-ever contextual machine translator in the Coptic language. Using state-of-the-art techniques in low-resource machine translation, we
demonstrate strong performance in translating unseen texts from a diverse range of religious sources.
We also provide new translations for over 5700 previously untranslated sentences, completing translations for 109 Coptic texts. The model already demonstrates potential for utilization by historians,
scholars of Early Christianity, and Egyptologists, as it automatically delivers the first-ever translations
for a range of ancient religious texts. Ultimately, we aim to open-source the model code and host ourtranslator for free use in the public domain1 . In doing so, we hope to advance historic literature, assist
in revival programs, and improve public fluency.

1. Website will be released at https://coptictranslator.com.

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2. Preliminaries
Our approach uses deep neural networks and Transformers to leverage recent technological advancements in neural machine translation. On a high level, we fine-tune an existing Transformer-based
multilingual model supplied on the Hugging Face library on parallel Coptic data.
2.1 Neural Machine Translation
Machine translation is the task of translating a sentence from a source language (for example, Coptic)
to a sentence with the same meaning in a target language (for example, English). Transformers are
the state-of-the-art architecture in neural machine translation (NMT), or machine translation using
deep learning (Vaswani et al., 2017). The attention mechanism trains translation models to learn two
components:
1. How to encode sentences from the source language into a k-dimensional vector, representing
the semantic meaning of the source sentence,
2. How to decode sentences from a semantic meaning vector into a sentence in the target language.
2.1.1 TRANSFER LEARNING
By training transformer-based NMT architectures on extremely large parallel datasets between languages, translation models can achieve near human-level performance (Haddow et al., 2022). However, for low-resource languages like Coptic, traditional training techniques would result in extremely
poor-performing models due to the lack of data. In 2016, (Zoph et al., 2016) found that by initializing
the parameters of the NMT architecture to that of a “parent model” trained on high-resource language
pair, and training the resulting model on the low-resource language pair, the performance of the translation model will substantially improve. In other words, Transformers can “transfer” knowledge abouttranslation from one language to another by fine-tuning the high-resource model on the low-resource
data.
2.1.2 BACKTRANSLATION
In some cases, a language may have a large corpus of monolingual sentences, most of which are unannotated. In these cases, translation quality can often be improved using a technique known as backtranslation (Sennrich et al., 2016). The technique involves using the existing parallel corpus to traina model that translates the source language to the target language, then using that model to translatea high-resource monolingual corpus of the target language. These synthetic translation pairs are thenused as additional training data for the original model.
2.2 Evaluation Metrics
When creating a translation model, it is crucial to develop effective evaluation metrics to judge thequality of the model translations. For our project, we consider two popular evaluation metrics: BLEU,
and chrF.
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2.2.1 BLEU
BLEU is a popular metric that measures the similarity between the machine-translated text and one ormore reference translations. It operates by comparing n-grams (sequences of n consecutive words) inthe machine translation to n-grams in the reference translation. BLEU calculates a score based on the
precision of matching n-grams, adjusting for the length of these n-grams to ensure translations don’t
overly favor longer phrases. Higher BLEU scores, closer to 1, indicate a better alignment between themachine translation and the reference(s). BLEU scores are generally reported out of 100 rather than 1.
In practice, sentences between languages can be translated in different ways using different synonyms.
However, BLEU only compares the words between the translated sentence and the reference, potentially resulting in low scores even when the two sentences capture the same meaning. Even so, BLEU
has been shown to correlate well with subjective human perception of translation quality, particularly
when multiple reference translations are available.
2.2.2 CHRF
Although BLEU works well in languages such as English, some languages, such as Coptic, have different syntactic structure incompatible with BLEU. Coptic is agglutinative, meaning that words in thelanguage can be composed of connected strings of arbitrarily long “lemmas”, or units of meaning.
Researchers at Google Research found that agglutinative languages are unfairly penalized by BLEU
(Siddhant et al., 2022). A single incorrect character or lemma within a word results in a completely
incorrect n-gram evaluation by BLEU, even if most lemmas within the word are correct.
chrF, a popular character-level adaptation of BLEU, provides more representative scoring translation quality in the agglutinative case (Popović, 2015). The chrF metric operates on a character level,
considering character n-grams instead of word n-grams like BLEU. The upshot is that chrF moreclosely measures similarity between translations and references in agglutinative languages since it appropriately considers the closeness of spelling (and therefore, lemmatization) between words. The
metric has been shown to correlate with human perception of translation quality better than BLEU
(Popović, 2015). Since the NMT literature often records BLEU within their results, for comparison
purposes, we typically provide both BLEU and chrF scores when evaluating translation quality.

3. Data
We obtained our data from the Coptic Scriptorium, which contains the largest parallel corpus for the
Coptic language. All texts are translated by experts in the field. Original Coptic texts contain no spaces,
but the Scriptorium normalizes the words and delimits them with spaces. The normalized-spaced
sentences are labeled “norm_group”. Additionally, the Scriptorium further tokenizes “norm_group”
into more subwords, which they label “norm”. This data was provided in a grid-formatted file that weparsed into a CSV file.

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eng

She labored to stop him, but she was not able to.

normⲛⲧⲟⲥ ⲇⲉ ⲁ ⲥ ϩⲓⲥⲉ ⲉ ⲥ ϣⲱϣⲧ ⲙⲙⲟ ϥ ⲙⲡⲉ ⲥ ϣ ϭⲙϭⲟⲙ ⲉⲣⲟ ϥ

norm_groupⲛⲧⲟⲥ ⲇⲉ ⲁⲥϩⲓⲥⲉ ⲉⲥϣⲱϣⲧ ⲙⲙⲟϥ ⲙⲡⲉⲥϣϭⲙϭⲟⲙ ⲉⲣⲟϥ

unnormalizedⲛⲧⲟⲥⲇⲉⲁⲥϩⲓⲥⲉⲉⲥϣⲱϣⲧⲙⲙⲟϥⲙⲡⲉⲥϣϭⲙϭⲟⲙⲉⲣⲟϥ

norm_romanizedntos deie a s hiauseie eie s shoousht mmo f mpeie s sh shmshom eiero f

norm_group_romanizedntos deie ashiauseie eiesshoousht mmof mpeiesshshmshom eierof

unnormalized_romanized

ntosdeieashiauseieeiesshooushtmmofmpeiesshshmshomeierof

norm_greekifiedντοσ δε α σ hισε ε σ sωsτ μμο f μπε σ s cμcομ ερο fnorm_group_greekifiedντοσ δε ασhισε εσsωsτ μμοf μπεσscμcομ εροfunnormalized_greekifiedντοσδεασhισεεσsωsτμμοfμπεσscμcομεροfTable 1: Example row in dataset, after preprocessing and augmentation
3.1 Data Cleaning
Before creating any additional columns, we preprocessed all existing data in the dataset:• We removed all rows where the Coptic field was empty.
• We identified all occurrences of accidental Latin characters in the Coptic field and replaced themwith their associated Coptic letter.
• We removed all special characters from the Coptic text.
• We replaced instances of “&apos” to an apostrophe and the “·” character to regular periods.
• We filtered out all Coptic text with English interjections or with missing text denoted by “[...]”.
Prior to preprocessing, our dataset included 33364 rows. After filtering, we retained 32535 rows.
3.2 Coptic Transcription
Because we fine-tuned an existing multilingual translation model, the model vocabulary was fixed. In
other words, the tokens input into the model could not change. Since the model did not include Coptic
characters in its vocabulary, we were unable to pass raw Coptic as input into the model.
3.2.1 ROMANIZATIONTo resolve the above problem, we decided to take the approach of Amrhein and Sennrich to “romanize” the Coptic text into Latin characters (Amrhein and Sennrich, 2020). Using the uroman libary2 ,we created additional columns “norm_romanized” and “norm_group_romanized” consisting of theromanized representations of the respective columns. The uroman algorithm uses a (potentially nonreversible) greedy dictionary mapping to map Coptic characters into associated strings in the Latinalphabet - see Table 1 for more information.
2. https://github.com/isi-nlp/uroman

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3.2.2 GREEKIFICATION
Additionally, since the closest related language to Coptic is Greek, we developed our own, reversible
dictionary mapping to transcribe Coptic characters into their closely related Greek counterparts. Sincesome Coptic characters have no Greek equivalent, we transcribed these characters into English letters.
Using Greek transcription, we generated the columns “norm_greekified” and “norm_group_greekified”- see Table 1.
Lastly, we removed spaces and punctuation from the norm_group to obtain the “unnormalized”
column and applied romanization and greekification to this column as well. Table 1 shows an example
row from the final data set.

4. Training And Validation Of Models
4.1 Pretrained Models
Since our approach relies on using transfer learning (see 2.1.1), we used pretrained massively multilingual translation models. For our Coptic-English translator, we used Helsinki-NLP/opus-mt-mul-en
3 , and for English-Coptic, we used the Helsinki-NLP/opus-mt-en-mul4 . Both models use encoderdecoder transformer architectures, trained on translation for 119 different languages.
We separated our data into a training set with 32136 rows, a validation set with 145 rows, and atest set of 254 rows. We chose validation and test sets that were from different sources and translatedby different people as shown in Table 5 and 6.
4.2 Parameter Tuning
We ran extensive experiments to generate models using multiple techniques on each Coptic columnof our dataset to see which one yielded the highest results. Many model parameters were fixed by themultilingual “parent” models, so we tuned the following train parameters:
1. label_smoothing = 0
2. num_train_epochs = 24
3. base_learning_rate = 5e−5

5. Results
5.1 Baseline
Since there are no machine translation systems for Coptic, we implemented a dictionary baseline,
using existing Coptic dictionaries to develop a word-by-word translation algorithm. Like all languages,
Coptic words and meanings depend on context, so we expected our baseline to perform poorly andwith high bias relative to an effective contextual model.
3. https://huggingface.co/Helsinki-NLP/opus-mt-en-mul
4. https://huggingface.co/Helsinki-NLP/opus-mt-mul-en

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Old Testament

New Testament

ZeldesBudge

dictionary_baseline

0.14, 14.49

0.23, 15.76

0.29, 10.29

0.10, 14.44

norm_romanized28.25, 54.64

61.73, 73.63

19.46, 36.37

21.71, 43.42

norm_greekified

26.97, 52.68

48.76, 64.20

15.30, 32.54

20.65, 41.44

norm_group_romanized

26.3, 51.49

43.11, 59.1

17.95, 33.47

18.49, 39.44

norm_group_greekified

28.30, 53.48

60.87, 72.19

14.34, 33.19

21.25, 41.48

unnormalized_romanized23.97, 49.48

44.02, 61.03

14.12, 32.07

16.71, 36.60

unnormalized_greekified

21.69, 46.96

43.64, 60.27

12.06, 30.85

16.00, 35.85

Table 2: BLEU, chrF scores of Coptic-English models on each test set

For our Coptic-English baseline, we split the Coptic into each norm unit and naively mapped each
unit to its dictionary definition.
However, since the English definitions of words for our Coptic dictionary are phrases, we could notuse a naive mapping-based approach like our cop-eng baseline translator. Instead, we emulated theexisting Coptic dictionary5 functionality by implementing semantic search. We encoded each definition
into a vector using the bert-base-cased6 model and normalized the embeddings using softmax.
Then, given an English input word, we find the definition whose vector is closest to the input wordembedding, and map it to Coptic word associated with that definition.
5.2 Model Results
We evaluated our cop-eng and eng-cop models on a variety of test texts, listed in Table 6. Eachcategory of test data was translated by different authors with stylistically different translations, leading
to significant variation within the results. Even so, the relative performance of all models on the test
data was consistent with the model performance on validation data. All contextual models strongly
outperform the dictionary baselines, demonstrating that even in an extremely low-resource scenario,
NMT models can effectively incorporate contextual clues to improve translation quality.
5.3 cop-eng model results
In the cop-eng direction, our translator obtains the highest BLEU on the New Testament data. We
hypothesize that the New Testament contains the highest quality and most internally consistent data.
It is also possible that the decoder of the parent model has already been trained on New Testament
sentences and thus provides better translations.
We consistently observe the best results from the norm_romanized model. The norm models
perform best because norming provides a tokenization preprocessing that enforces more semanticallymeaningful word units. We also hypothesize that the romanized models perform better than thegreekified models since the parent model is trained on more Latin alphabet data than Greek.
5. https://coptic-dictionary.org
6. https://huggingface.co/bert-base-cased

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ANCIENT VOICES, MODERN TECHNOLOGYOld Testament

New Testament

ZeldesBudge

dictionary_baseline

0.18, 20.12

0.19, 19.03

0.40, 15.66

0.20, 18.72

norm_romanized24.77, 56.71

40.19, 66.19

31.13, 57.45

30.24, 57.71

norm_romanized_bt

25.47, 56.72

41.23, 67.40

21.71, 51.70

31.29, 59.13

norm_greekified

23.24, 44.62

40.05, 58.33

23.83, 39.72

29.09, 48.65

norm_greekified_bt

25.48, 46.63

41.33, 59.20

27.11, 44.52

30.93, 49.82

norm_group_romanized

8.12, 56.30

14.42, 66.19

5.77, 52.89

9.94, 56.82

norm_group_romanized_bt

7.57, 57.05

15.85, 66.34

9.35, 50.84

11.64, 57.38

norm_group_greekified

5.36, 45.80

16.12, 57.64

4.88, 43.89

11.22, 48.51

norm_group_greekified_bt

6.78, 44.99

15.95, 58.23

6.01, 44.49

10.10, 49.20

Table 3: BLEU, chrF scores of English-Coptic models on each test set

To qualitatively evaluate the norm_romanized model, we provide an example excerpt (part 3)of the first-ever English translation of the historic Coptic text “Life of Lucius and Longinus”, by an
unknown author. The translation is coherent and is about the life of a virtuous saint called Longinus
who, having raised sick people from the dead, is glorified while visiting a monastery. We provide thetranslated text in Appendix 8.2.
5.4 eng-cop model results
The backtranslated models (xxx_bt) perform best, consistent with results in the literature (Sennrichet al., 2016). In addition, the norm based models achieve the highest performance, likely due tothe tokenization advantage. We do observe a marked decrease in BLEU on norm_group models incomparison to the cop-eng norm_group models. This is however an artifact of the way BLEU penalizes by word as mentioned in Appendix 2.2.1. Since we have no way of merging norm units backto norm_groups, the norm_group models will be the most useful for providing full translations ofEnglish sentences.

6. Ablation Study
6.1 Dialect Differences
The Coptic language contains numerous dialects. Our translation model is trained chiefly on sentencesfrom the Sahidic dialect since it had the most data available, however Bohairic is the primary dialect
in use today among the Coptic community (Winand, 2022). To evaluate our model performance on
Bohairic Coptic, we manually collected a small parallel dataset of 10 sentences (Isshak). We saw thebest performance on our norm_group_romanized model with a BLEU score of 2.92, and a chrF
score of 24.85. These results are expected given that Bohairic and Sahidic generally use different
spellings of the same words and some times entirely different words for the same meaning.
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RTT
chrF

Example Sentence

Example Round-Trip Sentence

Random Text

7.77

ihhe rcsnb a targwuw rnhosi zfwnk iegyk
lltizbx.

eiereie p thua deie m p rhs shouao eievol
tooun. eiereie p thua m p rhs sheiephtooun.

Green Eggs andHam, Dr. Seuss

33.91

Would you like them in a house?

Thou wishest them in a house.

CopticWikipedia

24.66

legal documents and personal lettersand letters, and letters,

Tobit 1-3

46.72

The prayer of both was heard before theglory of the great God.

and they heard the two hymns before theglory of the great God.

Tobit Train Data

56.22

Tobias then answered and said, Father, I
will do all things which thou hast commanded me:

Tobias then answered and said, father,
I will do all that thou hast commandedme.

Table 4: Analysis of round-trip chrF on texts of various domains (children’s books, technical documents, biblical books)

6.2 Colloquial vs. Religious Contexts
Since the translator was trained entirely on a select few religious texts, the model may perform poorly
on colloquial sentences. Unfortunately, due to absence of labeled data, we can not directly measure theperformance of our models on non-religious sources. Thus, we ran an analysis using heuristic measuresto quantify our model performance on out-of-domain data.
We measured performance using an alternative metric: round-trip chrF (Zhuo et al., 2023). In
essence, we measure translation competence by translating an input English sentence into Coptic, thentranslating back to English. We then compare the resulting, “round-trip” sentence with the original. Intuitively, the model will heavily distort sentences which contain unseen vocabulary or style, as observed
by 7.77 chrF from gibberish sentences from Table 4. Technical texts and children’s books also display
moderate distortion. However, round-trip translations on religious texts are distorted much less. In
conclusion, we heuristically demonstrate that despite having comparatively strong performance in thereligious domain, our model may not yet be appropriate for use in translating technical or vernacularEnglish sentences.

7. Discussion and Conclusion
Our models represent an exciting direction towards using AI in order to advance academic subfields
such as history, religious studies, and linguistics. We hope to further inspire research directions inthese areas.
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7.1 Real-World Application
We recommend the use of our models in real-world contexts, for translating historic, religious Sahidic
Coptic texts. However, once deployed, it is important for Coptic researchers to consider the following
caveats:• Our best Coptic-English model, norm_romanized, requires preprocessing. Thus, rather than
inputting into the model unnormalized Coptic text, it is necessary to tokenize the Coptic data into
the norm format using the tokenization tools provided by the Coptic Scriptorium, then romanize
the Coptic using the uroman package as described in Section 3.
• Like many NMT models, our model is subject to hallucination when encountering unknown
vocabulary. Relatedly, although our models achieve high accuracy on some domains, they are
subject to catastrophic failure outside of this domain or on more difficult sentences, and all
outputs should be treated with skepticism.
7.2 Limitations
7.2.1 STYLISTIC CONSISTENCY
Substantial sections of our train set contain translations which differ drastically in style from othersections of our train data. Our models learns to emulate these stylistic differences and thus oftentimes
outputs English which is not stylistically consistent. Furthermore, our models tend to output sentences
extremely religious in nature, and oftentimes output sentences in the Early Modern English style.
7.2.2 REPETITIVE TRANSLATIONS
Qualitatively, we observe many repetitive translations outputted by the Coptic-English models. For
example, the norm_romanized Coptic-English model outputs the following translation:

My Heart Is Crushed MONB.FL 105-106: And it shall come to pass in the second
month, on the fourth day of the month, on the four and twentieth day of the month, on
the four and twentieth day of the month, on the four and twentieth day of the month, on
the four and twentieth day of the month, ...

Repetitive translations are a common problem faced by low-resource machine translation systems.
The problem can be partially alleviated by sampling translations by the best overall rather than greedy
by the best token.
7.2.3 MODEL DOMAIN
We have demonstrated in subsection 6 that our model performs much better in historic and religious
contexts. Therefore we provide this model as a useful tool for analyzing historic texts, but caution
against its use in other contexts.
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7.3 Future Research Directions
Low-resource neural machine translation is an active field of research. Our research problem opens
unique and unsolved challenges in the following domains:• extending translation models beyond their domains when monolingual and parallel data are extremely low-resource• optimizing stylistic consistency in an extremely low-resource setting• translating historic texts in extinct languages, and evaluating the confidence and quality of theresulting translations
We believe that dictionary-based data augmentation improve out-of-domain generalization of themodel. In addition, using part-of-speech tagging provided by the Coptic Scriptorium could also improve the performance of our model. Furthermore, our model could become more consistent if translation data was preprocessed using a language model to convert sentences into more stylistically consistent formats. Lastly, OCR (optical character recognition) is actively being built for Coptic texts.
This research holds promise for building a much larger monolingual Coptic dataset, which could leadto greatly improved performance of eng-cop models using backtranslation. Thus, as the monolingual
Coptic corpus increases, we believe in the value of extending our model on this corpus.
In Section 6, we take for granted that round-trip translation is an appropriate measure of model
quality in a particular domain. Although (Zhuo et al., 2023) has supported the use of round-triptranslation for model evaluation, further research needs to be done to better understand its ability todelineate stylistic and domain limitations of a model.

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References
Chantal Amrhein and Rico Sennrich. On Romanization for model transfer between scripts in neuralmachine translation. In Trevor Cohn, Yulan He, and Yang Liu, editors, Findings of the Association for Computational Linguistics: EMNLP 2020, pages 2461–2469, Online, November 2020.
Association for Computational Linguistics. doi: 10.18653/v1/2020.findings-emnlp.223. URL
https://aclanthology.org/2020.findings-emnlp.223.
Richard Armstrong. Language death. URL https://engines.egr.uh.edu/episode/2723#:~:text=Historically%20speaking%2C%20the%20vast%20majority,languages%
20spoken%20in%20the%20world.
Aziz Suryal Atiya.
The coptic encyclopedia.
collection/cce/id/1039, 1991.

https://ccdl.claremont.edu/digital/

Barry Haddow, Rachel Bawden, Antonio Valerio Miceli Barone, Jindřich Helcl, and Alexandra Birch.
Survey of low-resource machine translation. Computational Linguistics, 48(3):673–732, September
2022. doi: 10.1162/coli_a_00446. URL https://aclanthology.org/2022.cl-3.6.
Nabil Isshak, February 2006. URL http://www.coptic.net/copticweb/contributions/
copticlanguagelessons.pdf. A course of lessons in Coptic language.
Bentley Layton. Coptic in 20 lessons: Introduction to sahidic coptic: With exercises & vocabularies.
Peeters, 2007.
Maja Popović. chrF: character n-gram F-score for automatic MT evaluation. In Ondřej Bojar, Rajan Chatterjee, Christian Federmann, Barry Haddow, Chris Hokamp, Matthias Huck, Varvara Logacheva, and Pavel Pecina, editors, Proceedings of the Tenth Workshop on Statistical Machine Translation, pages 392–395, Lisbon, Portugal, September 2015. Association for Computational Linguistics. doi: 10.18653/v1/W15-3049. URL https://aclanthology.org/W15-3049.
Caroline T. Schroeder and Amir Zeldes, 2013-2023. URL http://copticscriptorium.org.
Coptic SCRIPTORIUM.
Rico Sennrich, Barry Haddow, and Alexandra Birch. Improving neural machine translation models with monolingual data. In Katrin Erk and Noah A. Smith, editors, Proceedings of the54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers),
pages 86–96, Berlin, Germany, August 2016. Association for Computational Linguistics. doi:
10.18653/v1/P16-1009. URL https://aclanthology.org/P16-1009.
Aditya Siddhant, Ankur Bapna, Orhan Firat, Yuan Cao, Mia Xu Chen, Isaac Caswell, and Xavier
Garcia. Towards the next 1000 languages in multilingual machine translation: Exploring the synergy
between supervised and self-supervised learning, 2022.
NLLB Team, Marta R. Costa-jussà, James Cross, Onur Çelebi, Maha Elbayad, Kenneth Heafield,
Kevin Heffernan, Elahe Kalbassi, Janice Lam, Daniel Licht, Jean Maillard, Anna Sun, Skyler Wang,
Guillaume Wenzek, Al Youngblood, Bapi Akula, Loic Barrault, Gabriel Mejia Gonzalez, Prangthip
Hansanti, John Hoffman, Semarley Jarrett, Kaushik Ram Sadagopan, Dirk Rowe, Shannon Spruit,
Chau Tran, Pierre Andrews, Necip Fazil Ayan, Shruti Bhosale, Sergey Edunov, Angela Fan, Cynthia
11

ENIS, MEGALAA

Gao, Vedanuj Goswami, Francisco Guzmán, Philipp Koehn, Alexandre Mourachko, Christophe
Ropers, Safiyyah Saleem, Holger Schwenk, and Jeff Wang. No language left behind: Scaling humancentered machine translation, 2022.
UNESCO, editor. Atlas of the world’s languages in danger. Memory of Peoples Series. UNESCO, 3
edition, February 2010.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez,
Ł ukasz Kaiser, and Illia Polosukhin. Attention is all you need. In I. Guyon, U. Von
Luxburg, S. Bengio, H. Wallach, R. Fergus, S. Vishwanathan, and R. Garnett, editors, Advances in Neural Information Processing Systems, volume 30. Curran Associates,
Inc., 2017. URL https://proceedings.neurips.cc/paper_files/paper/2017/file/
3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf.
Jean Winand. Ucla encyclopedia of egyptology. UCLA Encyclopedia of Egyptology, June 2022. URL
https://escholarship.org/uc/item/8tr5w9nc.
Terry Yue Zhuo, Qiongkai Xu, Xuanli He, and Trevor Cohn. Rethinking round-trip translation
for machine translation evaluation. In Anna Rogers, Jordan Boyd-Graber, and Naoaki Okazaki,
editors, Findings of the Association for Computational Linguistics: ACL 2023, pages 319–337,
Toronto, Canada, July 2023. Association for Computational Linguistics. doi: 10.18653/v1/2023.
findings-acl.22. URL https://aclanthology.org/2023.findings-acl.22.
Barret Zoph, Deniz Yuret, Jonathan May, and Kevin Knight. Transfer learning for low-resource neuralmachine translation. In Jian Su, Kevin Duh, and Xavier Carreras, editors, Proceedings of the 2016
Conference on Empirical Methods in Natural Language Processing, pages 1568–1575, Austin, Texas,
November 2016. Association for Computational Linguistics. doi: 10.18653/v1/D16-1163. URL
https://aclanthology.org/D16-1163.

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8. Appendix
8.1 Validation and Test Data Sources
Category

SourceOld Testament
New Testament
Budge

Deuteronomy_1
Mark Chapter 1
Martyrdom of Victor part 8

Table 5: Validation set sources
Category

SourceOld Testament
New Testament
Budge
Amir Zeldes

09_I Samuel_17
42_Luke_1
Letter of pseudo-Ephrem MERC.AT 126-134
Acephalous 22: YA 517-518
Table 6: Test set sources

8.2 Life of Longinus and Lucius Part 3
Using norm_romanized_cop_eng, we provide a translation for Part 3 of the previously untranslated
Coptic text “Life of Longinus” by an unknown author7 . The text appears coherent, narrating the taleof a virtuous saint called Apa Logos who flees a monastery after he becomes glorified by the othermonks.
The country of Apa Logos, which he knew well, was a merchant in the province of Lacia,
and he used to ask questions in his monastery. And it came to pass that when he had cometo Rakote because of the need for his pragmatia, he went to the shrine of Saint Victor,
and he prayed, and he also desired to go to the monastery of the blessed Gaius, the elder
according to his custom. having great faith in the place where he lived, When he hadcome, and had received the blessing, he asked him to pray with him. When he was sittingat the door of the assembly, the holy man Gaius related many words of instruction fromthe Holy Scriptures to the salvation of his soul. And the Pragmatist looked, and saw Apa
Mothius, who had raised the dead, and had performed many miracles in the country of
Lycia, and he also confirmed that he was not himself, but that he was like unto him. For
the hair had grown old on him, and he was in great pain, therefore he knew him not: anda sign had been on his face from his youth, and when the Pharao had looked intently at
the sign which was on his face, he said to the holy man Gaius, ’Do you have such a great
man in this Monastery that he was filled with strength?’
The prophet said to him, ’Who among the brothers is this that you say about him? The
gatekeeper said to him, ’I speak as to this man who knocks at the door.’ For this Apa
7. https://data.copticscriptorium.org/texts/lifelonginuslucius/life-of-longinus-and-lucius-part-3/norm

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Logos, who lived in the country of Lycia, he and his father, Apa Lucius, who had raised
the dead, and had cast out demons from among the people and multitudes of the healed
ones, God worked through him in that country. When his fame went out into that land,
he was glorified by all. And he forsook his father, and fled secretly to this place, and fledfrom the vain glory of men. And when he had said these things concerning the holy man
Apa Logos, he was in the place of the gate which was sealed, and the governor cried out
to him, saying, Why didst thou hide thyself from us, and didst not tell us that thou wast
a monk? And straightway the humble man in his heart cast himself down under the feetof the Gaiusite, and repented, saying, ’Forgive me, O my father.’ And straightway theProphet commanded the hair of his head to be shaved, and he prayed over him, and hegave over him the crown and the Devil, according to the garb of Rakote, and when thebrethren who were in the monastery heard these words concerning Apa Logos, they set
him before them as a great perfect man, and they began to ascribe glory unto him exceedingly. And the holy man Apa reasoningos, when he saw that the men were praising him,grieved greatly. But the younger did not remember the word which his father, the Spirit,
gave him, saying, ’Let us flee from the glory of vain men.’
Then he took counsel in himself, saying, I have forsaken my country for the sake of theglory of men, and have departed to my father, and how can I remain in this place, whereas
this great multitude has magnified themselves against me? For this reason he came forthfrom that monastery, where very few God-loving men built for him, or a monastery by thesea. He made himself worthy to work the school, and to suffer for the work of his hands,
so that it might be found by those who labor in the work of his hands, which is like thework of the hands of the Apostle Paul, saying, ’These hands have ministered to my necessities and to those who are with me, and that I may find more happiness in giving thanin receiving.’ Many of the captain of the ship went up to him, having taken his armour,and had made them with his holy hands, and fastened them in the midst of their sails, as
far as bows, and believed. He prayed for them, and the Lord delivered their ships into thesea. After a little while the three who were monks came with him, and made disciples ofhim.
Sitting with his disciples, and meditating on the word of God, and working with his hands,
he sat down on his seat in the place where he was working, in the memory of the prophet
David, saying, Let me not sleep with mine eyes, I pray thee, with my tears. While he wassitting by night, sleep fell upon him, and he was exhausted: and if a man should stand
before him, saying, Rise up quickly, and go to the other side of the sea, and thou shalt
find thy father Apa Lucius, and he shall come forth to thee from the land. And he roseup straightway, and did not shew any one of his disciples, and he came to the place which
he had been told in the revelation, and he found the holy man Apa Lucius who had comedown from the boat immediately after they had saluted each other, and the old man said
unto him, ’I did not tell thee that I was going to the Christ, and that I should come tothee, and that the glory of God might bring them to the light, and that the waters might
be divided into two parts, and that the wall might be divided into two parts, and that thegates might be For the great and righteous trumpet of the prophets have called these men
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men Joel, because they are lovers of money, and they persecute the race of serpents likeenemies: so also are the holy ones, fleeing from the confusion of cities, dwelling in thewilderness, and they continue in the evil places of the good things of the world at all times,
which are the spirits of wickedness.
And they went on, and entered together. And his disciples were sitting and pondering thework, and they saw the holy man Apa reasonos walking with his father, and they marvelled, and they rose up with great joy, and saluted one another, and they prayed and sat
down. And Apa reasonings said to them, This is your father from this day forward: thenhe told them what he had done to them in the vision concerning him. And the holy man
Apa Lucius made Apa reasoning men to teach him how to do the work of the instructors, and they persevered zealously in the service of God, and they lived together in great
peace, each one keeping watch over his rank in every coast, so that the fame of their city,and their life, was filled with the great city of Rakote and all the country of Egypt.
For a woman, whose little daughter had an issue of blood, having heard of the resurrectionof the dead, has been cast forth.’ And when she had come to the other side of the sea, she
met the holy man Apa Logos, who was lying on the bank of the sea, and she asked him,saying, ’In what place is the servant of God, Apa Logos?’ He said, ’I want them to comewith me.’ And she informed him of the disease which was in it. He said to her, ’What
do you want?’ For neither was this woman independent of the man, nor independent of
the woman, but, ’My Lord Jesus Christ, be with you.’ She believed, and returned to herhouse. But when she had washed your feet, she went away naked. She went into the city,and told all the things which had happened to her by the angel of the Lord, saying, ’Whatkind of man is this?’ And she shewed them the token of his blessed countenance, andthey told her that Apa reasoningos had healed her. And the woman glorified God.
O what man is there that can speak of the healings and miracles which God hath performed? If I wish to speak to each one of them, he will not do so; for the time will comewhen I will let me speak, but I will declare of them a few of them, and I will declare tothee the completion of the word to this place, and I will spare some of the men of truth
because of the multitude of the miracles which they perform. For many came to him outof the city of Rakote, and out of her coasts, desiring to know the place where he was, andbrought to him all who were sick with various diseases, and he healed them all.

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