Exploring the Impact of Transliteration on NLP Performance: Treating Maltese as an Arabic Dialect
Year: 2023

Abstract:
Multilingual models such as mBERT have been demonstrated to exhibit impressive crosslingual transfer for a number of languages. Despite this, the performance drops for lowerresourced languages, especially when they are not part of the pre-training setup and when there are script differences. In this work we consider Maltese, a low-resource language of Arabic and Romance origins written in Latin script. Specifically, we investigate the impact of transliterating Maltese into Arabic scipt on a number of downstream tasks: Part-of-Speech Tagging, Dependency Parsing, and Sentiment Analysis. We compare multiple transliteration pipelines ranging from deterministic character maps to more sophisticated alternatives, including manually annotated word mappings and non-deterministic character mappings. For the latter, we show that selection techniques using n-gram language models of Tunisian Arabic, the dialect with the highest degree of mutual intelligibility to Maltese, yield better results on downstream tasks. Moreover, our experiments highlight that the use of an Arabic pre-trained model paired with transliteration outperforms mBERT. Overall, our results show that transliterating Maltese can be considered an option to improve the cross-lingual transfer capabilities.

heading: 1 Introduction
text: The availability of multilingual models has facilitated the development of NLP tools for many lowresource languages. Their appeal not only comes from this universal language representation but also through leveraging data from related languages (Kondratyuk and Straka, 2019; Wu and Dredze, 2019; Conneau et al., 2020). Despite this, multilingual models may fall short especially for lowerresourced languages (Wu and Dredze, 2020; Muller
et al., 2021). In particular, Muller et al. (2021) show that the cross-lingual transfer capabilities are hampered due to script differences between the target language and the related language which is part of the multilingual model pre-training. However, they show that performance is dramatically improved by transliterating the target language to the same script as the related language. Unlike translation, transliteration is a relatively cheap process which maps characters in a given script to another.
In this work, we focus on Maltese, a lowresource hybrid/mixed language of Semitic origin but written in Latin script. Although it retains a strong Semitic, specifically Arabic, component in its grammar, it borrows heavily from Romance (Italian) and English. This motivates us to explore the impact that transliterating Maltese into Arabic script could have on a number of downstream tasks. Besides using large language models based on Arabic text, this experimental setup opens up interesting research questions about the impact of high ambiguity from Arabic orthographic choices, such as dropping diacritics which may lower outof-vocabulary, but also increase ambiguity.
Despite its Arabic roots, transliterating Maltese to Arabic script is not trivial because of the strong non-Arabic influences on Maltese and its evolution independent of the Arab world for a significant number of years (Sutcliffe, 1936; Borg and Azzopardi-Alexander, 1997). That said, Arabictransliterated Maltese can be deemed as an Arabic dialect with a higher degree of Italian codeswitching. As such, unlike Muller et al. (2021), we do not just rely on multilingual language models for cross-lingual transfer, but also make use of Arabic language models, specifically CAMeLBERT (Inoue et al., 2021). We compare its performance
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to multilingual BERT (Devlin et al., 2019) and monolingual Maltese BERT (Micallef et al., 2022). Empirically, we show that there are differences in the cross-lingual transfer capabilities of Arabic models and multilingual models for Maltese.
The main contributions of this work are as follows. We present various transliteration pipelines from Maltese to Arabic script ranging from simple one-to-one character maps to more sophisticated alternatives that explore multiple possibilities or make use of manually annotated linguistic constructions. We show that the sophisticated systems are consistently better than simpler systems, quantitatively and qualitatively. We also show that despite its hybrid nature, transliterating Maltese can be considered as an option to improve the cross-lingual transfer capabilities.
The rest of the paper is organized as follows. We present a discussion of motivating linguistic background (Section 2) followed by our approach for transliterating Maltese to Arabic script (Section 3). We present our evaluation in Sections 4 and 5.

heading: 2 Linguistic Background

heading: 2.1 Arabic and Maltese
text: Arabic and Maltese are closely related Semitic languages. Arabic is the national language of ∼360 million people across 22 countries (Eberhard et al., 2022), while Maltese is the national language of Malta and is spoken by ∼500,000 people (Rosner and Borg, 2022).
The Arabic language is a collection of coexisting varieties. While Classical Arabic (CA) still survives in Muslim religious ceremonies, Modern Standard Arabic (MSA) is the official national language of the media and formal education, but neither CA nor MSA is the mother tongue of Arabs today. A number of dialectal Arabic (DA) varieties are primary spoken (and increasingly informally written varieties). The coexistence of MSA and DA is described as diglossia (Ferguson, 1959). Foreign languages, particularly French (in the Maghreb) and English (in the Middle East) have a strong presence in DA and result in common code-switching (Hamed et al., 2020).
The Maltese language traces its origins to medieval Sicilian Arabic. In its current form, Maltese contains elements from Arabic, Italian, and most recently English. This reflects its geography and history: the Mediterranean island of Malta is halfway between Tunisia and Italy, and historically
it was under Arab rule from 870 to 1090 CE. Being the language of a Christian nation, Maltese has no CA influences, unlike other Arabic dialects with diglossia (Sutcliffe, 1936; Borg and AzzopardiAlexander, 1997). In some simplifying respects, Maltese can be seen linguistically as a dialect of Arabic with a higher degree of code-switching to Italian. Čéplö et al. (2016) reports that mutual intelligibility between Maltese, Tunisian Arabic (TA), and Libyan Arabic (LA) ranges between 30% and 40%, with TA having the highest level of mutual intelligibility with either of the other two varieties.

heading: 2.2 Script and Orthography
text: The most important difference between Arabic and Maltese is the use of Arabic and Latin scripts, respectively. Furthermore, the two languages use different orthographic philosophies in how to map linguistic features (phonology and morphology) to script letters. Given the topic of this paper and to facilitate the presentation of the remaining sections, we will start with a discussion of the two scripts and the orthographic philosophies they use.
The Arabic script is used to write a number of languages from different language families, e.g. Arabic (Semitic), Persian (Indo-European), and Uyghur (Turkic). The Arabic script is mostly used as an Abjad where diacritical marks represent short vowels and consonantal doubling, although there are exceptions such as Uyghur’s Arabic alphabet. Since most diacritics are optionally written in the context of the Arabic language (Abjad) (Habash, 2010), this leads to a high degree of ambiguity. It should be noted, however, that initial vowels are always marked by having a word initial Alif @ A as a diacritic carrier; and final vowels typically reflect some deeper morphological feature of the word such as a weak verbal root radical or a nominal feminine ending. Arabic effectively relies on its strong templatic morphology that allows readers to limit the ambiguity space. Arabic orthography also tends toward morphophonemic spelling which abstracts away from allomorphy, e.g. the Arabic definite determiner proclitic È@ Al has a number of allomorphs that assimilate with word-initial coronal consonants (so-called Sun Letters) but is always written in the morphemic form: Ò Ë@ Al+šms /aš+šams/ ‘the+sun’. Finally, while MSA has standard rules for orthography, DAs do not. Arabic NLP researchers have developed conventions for writing DA to allow studying spelling
varieties (Zribi et al., 2014; Habash et al., 2018).
The Maltese script is based on the Latin script with some extension ns (ċ, ġ, h̄, and ż). The Maltese orthographic philosophy is in some way diametrically opposed to Arabic’s more abstracting orthographic philosophy: Maltese tries as much as possible to reflect the phonological form of the words. There are a few exceptions to this principle, which are felicitous for our task. First, Maltese marks the form of the definite determiner with a hyphen. The number of determiner variants is quite large (∼150) due to allomorphy from phonetic assimilation and proclitics, e.g., il-, l-, ix-, x-, is-, s-, it-, t-, id-, d-, are just a few forms of the definite determiner, all of which map to Arabic È@ Al; this is in addition to many cliticized forms such as lill-, lix-, lis-, lit-, lid- (all with the preposition lil ‘for’) or tal-, tax-, tas-, tat-, tad- (all with the preposition ta’ ‘of’) (Sutcliffe, 1936). Second, Maltese writes some consonants to reflect their etymological link to Arabic, e.g. gh̄ which is mostly silent and corresponds to Arabic ¨/ ̈ E/g (Fabri et al., 2014). Third, Maltese spells the commonly used conjunction u ‘and’ separately from the word, whereas Arabic attaches it to the following word. Finally, Maltese has access to capital letters, a concept that has no parallel in Arabic script. Capital letters are used in Maltese in similar ways to English, marking proper nouns. We leave the use of capitalization as an additional modeling feature to future work.

heading: 2.3 Phonological Differences
text: Maltese lost many Arabic phonological features. These include all emphatic consonants (Walter, 2006), e.g. the s letters in Maltese sejf ‘dagger’ and sajf ‘summer’, correspond to two letters in their Arabic cognates, J sayf and J Sayf. Other examples include the Arabic voiced pharyngeal (¨ E) and voiced uvular ( ̈ g) merging into Maltese gh̄; and the voiceless versions of both (h H and p x) merging into Maltese h̄, among others. Many of the Arabic cognates in Maltese with the Qaf consonantal variable q are spelled in Maltese with q although pronounced as a glottal stop (as in Urban Levantine and Egyptian Arabic), e.g. Maltese triq ‘street’ K Q£ Tryq. Due to Italian influences, the Maltese phonetic inventory has acquired a number of non-Arabic sounds such as p, and v.
Maltese has six vowels (a, e, i, o, u, ie), the first five of which may be shortened in some contexts. Standard Arabic has three short and three long vowels; while most dialects expand the set to five short and long. Arabic short vowels are generally written with diacritical marks with exceptions due to underlying derivation, or word position (initial/final) (Habash et al., 2018).

heading: 2.4 Morphological Differences
text: Maltese morphology shares a lot of features with Maghreb Arabic morphology, and Arabic/Semitic
morphology in general. This include a rich inflectional space (person, gender, number, aspect) and many clitics that both Arabic and Maltese write as part of the word form. For example, Maltese dar ‘house’, a cognate of Arabic P@X dAr, can be inflected into id-dar ‘the house’, tad-dar ‘of the house’, f’dar ‘in a house’, and darha ‘her house’ which correspond to Arabic P@YË@ AldAr, P@YË@ ¨A K tAE AldAr, P@Y ̄ fdAr, and AëP@X dArhA, respectively. While Maltese marks the determiner with a hyphen, which provides a strong morphological signal (Section 2.2), it does not mark pronominal clitics or negation particles similarly. In this paper, we do not use any morphological analysis and disambiguation tools to help in transliteration. We leave this direction to future work.

heading: 2.5 Lexical Differences
text: While there are many shared words between Maltese and Arabic (especially Maghreb and Tunisian Arabic), there are important differences. Table 1 highlights some examples of both kinds, but these can be further broken down. First are words that undergo a major phonological shift, e.g. jafx ‘does not know’ comes from Arabic ̄QªK yErf$. Second are words that went through a semantic shift, e.g. h̄ażin ‘bad’ is related to Arabic áK Qk Hzyn ‘sad’. Thirdly, Maltese has many univerbation constructions which Arabic generally avoids, e.g. Maltese waranofsinhar ‘afternoon’ corresponds to three Arabic cognate words PAî DË @ ® K @Pð wrA nfS AlnhAr ‘after middle [of] the day’. Finally, Italian-origin words are all distinct from Arabic, although in some cases they may have cognates in the dialects, e.g. kċina ‘kitchen’ corresponds to TA é JJ k. ñ» kwjynh (Aquilina, 1987, 1990).

heading: 2.6 NLP Conventions in Arabic and Maltese
text: NLP research conventions have developed independently in Arabic and Maltese, posing challenges to working on them jointly. For example, basic tokenization in the Universal Dependency Treebanks for Arabic follows a relatively deep morphological tokenization that separates all clitics (except the determiner), and normalizes the form of the baseword (Nivre et al., 2017; Taji et al., 2017). In contrast, Maltese tokenizes the determiner but not much more, leaving all other enclitics attached to the word and proclitics attached to the determiner (Čéplö, 2018). We follow the Maltese decisions here to simplify our training and evaluation.

heading: 3 Our Transliteration System
text: We present a Maltese-to-Arabic transliteration system with a number of variants that we evaluate in Sections 4 and 5. The transliteration system contains two operations: mapping and ranking. Maltese text tokens and characters are mapped from Latin script to one or more alternatives in Arabic script (Section 3.2). Then, a separate component ranks the choices or uses a deterministic hardcoded baseline (Section 3.3).

heading: 3.1 Preprocessing
text: As discussed in Section 2.6, we operate on tokenized Maltese to allow us to maintain label alignments from the training data of the downstream tasks. As such, Maltese texts are first tokenized using the MLRS tokenizer.1 Next, all Maltese texts are lower-cased, since there is no casing information in Arabic; and all Latin script diacritics are removed, excluding those relevant to Maltese, namely: ċ, ġ, h̄, and ż. For example, soċjetà ‘society’, which is a remnant of Italian, is mapped to soċjeta, reflecting a common form of spelling such words in standard Maltese.

heading: 3.2 Character and Token Mappings
text: Character Mappings We list all of the Malteseto-Arabic character mappings we consider in Table 2. Most are letter-to-letter mappings such as k to ¼ k, but also include the Maltese multi-character letters ie and gh̄. The Basic column indicates the most expected letter mapping based on our observations considering etymology, phonology, and Arabic letter frequencies. The additional columns in the table indicate conditional mappings as well as non-deterministic additional mappings. For vowels, we include word-initial and word-final conditional mappings; and for consonants, the second of doubled letters may be mapped to a Shadda (Arabic gemination diacritic). All Arabic diacritics are deleted after the mapping step since they are often absent in the language model training data (Habash, 2010). We use the character mappings in two ways: (a) deterministic mappings using only the Basic column and its associated Doubling column in Table 2, and (b) non-deterministic mappings using all the columns in Table 2. Our deterministic mapping does not apply context specific word-initial and word-final rules.
1https://mlrs.research.um.edu.mt/
( shaded regions are romanizations in the Buckwalter scheme (Habash et al., 2007)). Additional, Word Initial, and Word Final are the conditional alternatives considered for the non-deterministic mappings.
Token Mappings We augment the characterlevel mappings with closed class token-level mappings that exploit known features of Maltese orthography such as the spelling of the definite determiner, as well as the Zipfian head distribution of closed class words which we expect to help transfer learning in downstream tasks. In the settings where they are used, the token mappings take precedence over the character mappings since they match a token exactly.
We extracted all the closed class tokens from the training set portion of the MLRS POS data (Gatt and Čéplö, 2013), by filtering over the partof-speech tag. For each of these tokens, a manual transliteration is performed by native Arabic speakers following a consistent interpretation of the Conventional Orthography for Dialectal Arabic (CODA) guidelines (Habash et al., 2018). To facili-
tate the interpretation of the token, annotators were provided with the POS tag, the IPA transcription (extracted using a grapheme-to-phoneme system by Borg et al. (2014)), and a sample sentence where the token is used. Native Maltese speakers were consulted for ambiguous cases.
In total, we have 691 mappings (henceforth, Full closed-class).2 Examples include fuq PREP ‘over’, ftit QUAN ‘some’, kellu VERB_PSEU ‘he had’, and mhux PRON_PERS_NEG ‘he is not’, which map to ñ ̄ fwq, I J ̄ ftyt, éË àA¿ kAn lh, and ñëAÓ mAhw$, respectively. Additionally, we consider a subset of 135 mappings (henceforth, Small closedclass) which we restrict to the tokens containing - and/or ’, such as il- ‘the’, fis- ‘in the’, and t’ ‘of’, which map to È@ Al, ÈA ̄ fAl, and ¨A K tAE, respectively. We designate not using the token mappings as None.

heading: 3.3 Ranking Techniques
text: While token mappings are essentially deterministic, character mappings produce a large lattice of combinations, e.g. Maltese h̄ielsa ‘free’ results in 20 forms, including é ËA g xAlSp, úæ ËAg HAlSY, A ËA g xAlsA, é ËAg HAlSh, and Êg Hls. In this section we present various ranking techniques used to select one of the alternatives for a given input.
Deterministic The use of deterministic character mappings yields a single alternative for each word, thereby not requiring any ranking.
Random A random choice is made by selecting the first alphabetically sorted token from the list of combinations. We sort to keep this technique stable across different runs.
Sub-Token Count The BERT-based language models used in the downstream task may split a given word into multiple sub-tokens (Devlin et al., 2019). We choose the mapping combination with fewer resulting sub-tokens. This idea is based on the evidence that a tokenizer that splits tokens into fewer sub-tokens correlates with better downstream performance (Rust et al., 2021).
2We do not use the part-of-speech information in the actual mapping process. Although there are a number of tokens that appear with different part-of-speech tags, only one of these tokens (m’) resulted in different transliterations: NEG AÓ mA, COMP áÓ mn, and PREP ©Ó mE. Since the NEG reading is the most common in our data by far, we ignore the other two.
N-gram Language Model Scores We use the word and character n-gram language models from Baimukan et al. (2022) to get word-level and character-level scores on each generated token, respectively. As highlighted in Section 2.1, due to the similarity with Maltese, we consider both the country-level Tunisian (TUN) and region-level Maghrebi (MAG) models, ending up with two sets of scores.
Some of these scores are bound to produce ties, occasionally ranking more than one token in first place. In our analysis, the word n-gram model tends to produce ties whenever the word is out of vocabulary while the sub-token count model is much more sporadic with ties. We observed that the character n-gram score almost never produced ties on the data, and so we used it as a fallback to resolve ties. Ties can further be resolved randomly if need be.

heading: 3.4 Implementation
text: The various mapping settings we consider in the rest of the paper select for a token-mapping setup and a ranking setup. Conceptually, putting together all of these components results in a pipeline where a token is mapped using the closed-class token mappings, backing off to the character mappings whenever the token is not found in the token mappings. This is followed by a ranking step which selects among the various options produced by the mapping component.
We implement all mappings from Section 3.2 using finite-state machinery in Pynini (Gorman, 2016). The seven basic FSTs we implement are the following: full token mappings, small token mappings, non-deterministic and deterministic multicharacter mappings,3 non-deterministic and deterministic single character mappings, and dediacritization mappings. These are then composed in succession on the fly, based on the experimental setup being used.
The generated alternatives are ranked as described in Section 3.3. The sub-token count metric is implemented using the Transformers library (Wolf et al., 2020) while the n-gram language model scores are obtained using KenLM (Heafield, 2011).
We make the code publicly available.4
3This includes multi-character letters (ie and gh̄) along with geminates (Doubling), and Word Initial and Word Final vowels from Table 2.
4https://github.com/MLRS/malti_arabi_fst

heading: 4 Downstream Task Evaluation
text: The transliteration system is evaluated on three downstream tasks: Part-of-Speech Tagging (XPOS),5 Dependency Parsing (DP), and Sentiment Analysis (SA). Input tokens in the datasets are transliterated as discussed in Section 3, with their corresponding labels/tags remaining unchanged. Further details on the dastaset sources and processing is given in Section 4.1.
We consider three setups of token mappings, all of which use the character mappings as described in Section 3.2: the entire set of the full closed-class mappings (Full), the small closed-class mappings (Small), and no token mappings (None). For each of these setups, we use the ranking techniques from Section 3.3. This creates 24 distinct transliteration pipelines (3 mapping options by 8 ranking techniques), which we explore in this Section. Every dataset is transliterated through each of these pipelines, which are then used to fine-tune the language model following the setup used by Micallef et al. (2022). Each fine-tuned model is evaluated on the corresponding transliterated test set.
We systematically compare the pipelines on CAMeLBERT-Mix (Inoue et al., 2021) due to its training on dialectal data. We also fine-tune BERTu, a monolingual Maltese model (Micallef et al., 2022), and multilingual BERT (mBERT) (Devlin et al., 2019) on the datasets in the original script (untransliterated). Additionally, we consider another setup for mBERT where it is fine-tuned on transliterated Maltese. We report accuracy for XPOS Tagging, Labelled Attachment Score (LAS) for DP, and macro-averaged F1 for SA.

heading: 4.1 Datasets
text: We use the MUDT (Čéplö, 2018) dataset as is for the DP task. For the XPOS task, we use the MLRS POS dataset (Gatt and Čéplö, 2013), but with different splits from Micallef et al. (2022) to ensure that the instances overlapping with the MUDT data are in the same splits.
The SA dataset used (Martínez-García et al., 2021) is preprocessed and tokenized using the MLRS tokenizer. Although this task involves classifying a whole sentence, this preprocessing is done because the transliteration system operates on tokens rather than sentences. Once each token is transliterated these are joined back as a single text,
5XPOS refers to the language-specific tagset as opposed to UPOS, the universal tagset (Nivre et al., 2017).
separated by spaces. Admittedly, this results in different spacing compared to the source sentence, particularly for tokens with determiners and punctuation symbols in general. However, we fine-tune the baselines which use the original Latin script with this same pre-processing strategy.
The tokens in the MUDT and MLRS POS datasets are kept as is since these are consistent with the MLRS tokenizer. A summary of the dataset sizes is given in Table 3. To address the discrepancy in the data sizes, we also consider a lowerresourced setup where the training and validation (but not test) sets of each tasks are reduced to the smallest dataset size used in this evaluation (SA). This allows us to control for size when analysing the cross-lingual transfer capabilities.

heading: 4.2 Results
text: The results shown in Table 4 highlight that a combination of the full closed-class mappings and any of the non-deterministic systems achieve the best performance across all tasks. As expected, the deterministic system without any token mappings performs the worse, generally. Analysing the token and character mappings as different dimensions, reveals some interesting trends.
Token Mappings The inclusion of closed-class mappings consistently yields improvements over using no token mappings in all scenarios. In the deterministic case, the full token mappings are beneficial to surpass the non-deterministic counterpart with random ranking, in the DP and SA tasks, and are competitive against the other non-deterministic non-random scores in all tasks. The small closedclass mappings also generally improve over using no token mappings, although this is slightly detrimental in a few cases. Inspecting further the relationship between the full, small, and no token mappings, it is evident that the jumps in performance are more pronounced in the lower-resourced setup compared to the whole data setup. These findings indicate that while linguistic annotations are generally helpful, they are most useful in setups when data is scarcer.
Ranking Techniques The random ranking performs the worst of all the techniques considered for the non-deterministic character mappings but does better than the deterministic counterpart in cases where no additional tokens are present. All techniques, apart from random, perform comparably, with the word language model ranker achieving the best scores on the syntactic tasks while the character model and sub-token rankers give the best results in the semantic task.
Ranking with the Tunisian word model scores yielded the best result in 3 out of 5 task-data setups. This is likely due to the high degree of mutual intelligibility between Maltese and Tunisian Arabic as detailed in Section 2.1. Moreover, this ranking tends to give significant boosts in performance just by using the small token mappings as evidenced by the similar results obtained by the system with the full token mappings. Conversely, the Maghrebi models tend to give worse scores without any token mappings and gave a worse result than the deterministic system in one particular case in SA.
Pre-trained LMs and Transliteration Comparing the best result from each task and data size setup from Table 4 against the baselines shown in Table 5, it is evident that mBERT fine-tuned without transliteration is only better than the best transliteration pipeline in XPOS when the entire data is used. For SA and lower-resourced DP, the difference between mBERT and the best transliteration pipeline on CAMeLBERT is found to be statistically significant, using a 1-tailed t-test with a p-value of < 0.05.
In Table 5, we compare transliteration with the Tunisian word model ranking with full token mappings. It is clear that fine-tuning BERTu with the original (untransliterated) data yields the best performance overall, owing to the Maltese corpora that this model is pre-trained on. Inspecting the results obtained for mBERT, transliteration does not always improve performance compared to untransliterated fine-tuning, and can result in significant degradations as evidenced in the SA task. Since, this is counter to what Muller et al. (2021) reported, it could be attributed to the hybrid nature of Maltese. However, we posit that this is also due to the fact the mBERT was solely pre-trained on MSA. Conversely, CAMeLBERT-Mix was trained on 5.8 billion DA data, making up around a third of the entire pre-training corpus (Inoue et al., 2021). In fact, when fine-tuning with transliterated Mal-
Color shading is done with respect to the best and worst values of each task and training size setup.
tese, CAMeLBERT performs better than mBERT in most task-data setups. CAMeLBERT is also able to surpass or be very competitive with mBERT fine-tuned on raw Maltese. This finding gives further evidence that there is some level of mutual intelligibility between transliterated Maltese and dialectal Arabic. Moreover, making use of monolingual models should be considered for cross-lingual transfer, whenever this is available.

heading: 5 Human Readability Evaluation
text: In this section, we investigate how readable transliterated Maltese is to native Arabic speakers. We compare four settings: the deterministic system against the non-deterministic system (Tunisian Arabic Word Model) with both None and Full token mappings. We sample 50 instances from the MUDT training set (Čéplö, 2018). For each example, we provide the original sentence, a translation extracted from Google Translate as a reference, and each of the alternative transliterations (see example in Table 6). We hide the transliteration system in-
formation and shuffle the order in which each alternative is displayed to prevent biases. For this study we ask the evaluator, a native speaker of Tunisian Arabic, who is fluent in French and familiar with Italian, to rank the transliterations in the order of how readable the text is, where 1 is best.
Table 7 shows the average readability rank for different combinations. The results show that using the Word Model is better than the Deterministic model, and that using the token mappings is helpful. These results correlate with our empirical evaluation from Section 4.
The evaluator reported that reading Maltese written in the Arabic script allowed them to easily recognize shared words between Maltese and Tunisian Arabic. For instance, the evaluator did not recognize the Maltese word kien, but when transliterated into the Arabic script as àA¿ ‘he was’, it was evident. However, Italian-origin words presented a reading challenge, e.g. Maltese akkuża (Italian accusa) was garbled. The evaluator also pointed out that none of the transliteration models were capable of handling
Maltese univerbations, such as waranofsinhar ‘afternoon’ (see Table 6), which made it challenging to recognize and read them accurately.
This experiment highlighted some of the many challenges in reading Maltese written in Arabic script and provided insights into the limitations of different transliteration models, and issues to consider addressing in the future.

heading: 6 Conclusion and Future Work
text: We presented a Maltese-to-Arabic transliteration system as a tool to leverage cross-lingual transfer from Arabic. As evidenced by our empirical results, a non-deterministic system with signals from the target language helps in choosing a better transliteration alternative, especially in ambiguous cases. Moreover, incorporating human-annotated transliterations of a set of closed-class of words is beneficial in downstream performance, especially in lower-resource settings.
Our experimental setup exploited an Arabic language model for cross-lingual transfer, instead of a multilingual model such as mBERT. Results show promising results, giving better performance than multilingual models. This echoes the findings by Wu and Dredze (2020), and we encourage further
research to investigate ways to effectively leverage resources from linguistically related languages.
Future work should investigate the use of large sentence-level contexts in mapping selections. Exploring cross-lingual transfer from Italian and English is also an interesting direction, including the use of few-shot and zero-shot learning. It would be interesting to also investigate these cross-lingual transfer techniques through transliterated Arabic.
Limitations
In this work, we transliterate all Maltese words in the same manner. Given the hybrid nature of Maltese, it might be optimal to handle words which do not have an Arabic origin in a different way. Similarly, we do not treat named-entities any differently.
Moreover, we assume that the Maltese text is written using the standard orthographic rules. In turn, the system might produce spurious transliterations for cases with spelling errors. This issue also exists when the text is in raw form, but may be further exacerbated with transliteration. The character mappings could be expanded to handle dropped Maltese diacritics, such as writing c instead of ċ, but there are other cases where silent letters such as gh̄ are dropped altogether, making the problem non-trivial.

heading: Acknowledgements
text: We acknowledge LT-Bridge Project (GA 952194) and DFKI for access to the Virtual Laboratory. We further acknowledge funding by Malta Enterprise.

heading: Future Work
text: Future work should investigate the use of large sentence-level contexts in mapping selections. Exploring cross-lingual transfer from Italian and English is also an interesting direction, including the use of few-shot and zero-shot learning. It would be interesting to also investigate these cross-lingual transfer techniques through transliterated Arabic. Limitations
In this work, we transliterate all Maltese words in the same manner. Given the hybrid nature of Maltese, it might be optimal to handle words which do not have an Arabic origin in a different way. Similarly, we do not treat named-entities any differently. Moreover, we assume that the Maltese text is written using the standard orthographic rules. In turn, the system might produce spurious transliterations for cases with spelling errors. This issue also exists when the text is in raw form, but may be further exacerbated with transliteration. The character mappings could be expanded to handle dropped Maltese diacritics, such as writing c instead of ċ, but there are other cases where silent letters such as gh̄ are dropped altogether, making the problem non-trivial