InsightsResearch
How the Gulf writes: Arabizi, code-switching and dialect in business messages
What operational messages in the Gulf really look like, what the research says about reading them, and what that asks of an AI agent that acts on them.
Abstract
Operational messages in the Gulf arrive in Gulf Arabic written in Arabic script, in Arabic typed with Latin letters and numerals (Arabizi), in English, and often in a mixture of all three, with more than one digit system, and sometimes as voice notes. We ask what this means for an AI agent that has to read such messages and act on them. We review the published research on Arabizi detection and transliteration, Arabic-English code-switching and Gulf dialect resources, set it beside official population statistics for the six GCC states, and annotate an illustrative message word by word. We find that resources for Gulf text and speech exist but are thin and uneven, that tools and models built for Modern Standard Arabic lose accuracy on Gulf input, sometimes to the point of being unusable, and that public evaluation sets rarely look like a Gulf business message. We propose a reading pipeline (normalise, detect script and dialect, interpret, confirm in the customer's language) and argue that an agent should be judged on messages shaped like the customer's own, and should never require a customer to switch to Modern Standard Arabic or English to be understood.
Key findings
- Gulf Arabic was already being typed in Latin letters and numerals in 2003, and surveys in Oman and Saudi Arabia find that mixing Arabic and English is ordinary among educated and working writers [1, 2, 3].
- The people writing are diverse: in national statistics compiled for mid-2022, non-nationals were a majority of residents in four of the six GCC states and 54.6% across all six [4].
- Tools built for Modern Standard Arabic lose accuracy on Gulf input: a tagger built for MSA got 64.03% of lemmas in Gulf text right, against 77.02% for one built for Egyptian Arabic, and off-the-shelf speech models exceeded 100% word error rate on Emirati podcast speech [5, 6].
- Public evaluation rarely looks like the Gulf: the 2024 multi-label dialect identification test set had no GCC dialect among its labels, and a 2025 study of Arabizi translation by large language models covered no Gulf dialect [7, 8].
- An agent should normalise, detect, interpret and then confirm in the customer's own language, and should be evaluated on the customer's past messages, stratified by script, dialect and channel, rather than on one average over clean text.
1Introduction
Most of what moves a Gulf company's day is written in a hurry. A buyer confirms an order from a car park, a driver reports a carton short from a loading bay, a supplier moves a delivery date in a voice note. These messages decide what is shipped, invoiced and chased, and they are what an AI agent has to read before it can do anything useful in operations. Very often they are not written in the Arabic that language technology was built for.
Arabic is diglossicUsing two varieties of one language for different purposes: here Modern Standard Arabic for formal writing, and a spoken dialect for almost everything else.: Modern Standard Arabic (MSA) is the written standard across the Arab world, while people speak, and increasingly type, their own dialects [5, 9]. In the Gulf three further facts sit on top of that. People have long typed Gulf Arabic in Latin letters and numerals, a practice called ArabiziArabic written in Latin letters, with numerals standing in for Arabic letters that have no Latin equivalent, such as 3 for ع, 7 for ح and 2 for ء. [1]. Many writers move between Arabic and English inside a single message [2, 3]. And in four of the six GCC states most residents are not nationals, so the person at the other end of a thread may write another dialect, Arabic as a second or third language, or English [4].
This matters more now than when language models were mainly used for conversation. An agent that confirms orders, releases credit or reschedules deliveries turns a misreading into an operational error: three cartons become two, tomorrow becomes today, a question becomes a commitment. Our question is therefore practical. What do operational messages in the Gulf look like, what does the published research say about reading them, and what does that require of an agent that acts on them?
This paper contributes:
- A review of the research on Arabizi, Arabic-English code-switching and Gulf dialect resources, read for what it implies about business messages rather than social media.
- The demographic context from official statistics, cited country by country with its year, and what it means for who writes to a Gulf company.
- An annotated illustrative message, and a matrix of the phenomena such messages contain against what each requires of a model.
- A reading pipeline (normalise, detect, interpret, confirm in the customer's language) and an evaluation practice built on the customer's own past messages.
2Background and related work
Gulf Arabic and its spelling
Khalifa and colleagues define Gulf Arabic broadly as the Arabic spoken in the six GCC states, and stress that the region's cultural homogeneity does not make it linguistically homogeneous: the KhaleejiGulf Arabic: the family of dialects spoken by nationals of the GCC states, which differ among themselves in sounds, grammar and vocabulary. dialects differ from one another in sound, grammar and vocabulary, and all of them differ from MSA [5]. None has an official spelling. The unified CODA guidelines, which set conventions for 28 city dialects from Rabat to Muscat, start from that absence: since no dialect has a standard orthography, no spelling of a dialect word can be called incorrect [10].
Arabizi
The earliest Gulf study we found is Palfreyman and Al Khalil's analysis of instant messages written by female university students in the UAE [1]. The writers used numerals for Arabic sounds with no Latin counterpart (5 for the sound of خ, 6 for ط, 9 for ص, 7 for ح), wrote ch and g for Gulf pronunciations that MSA spelling does not show, and in one message used the numeral 3 both as a letter and as the number three. Of the 79 people who answered the authors' survey, 55% said they wrote this way because it was easier to type in English, 30% cited missing support for Arabic script and 15% the Gulf sounds, and some described it as a social choice. The numbers are small and the study is two decades old. But Darwish noted in 2014 that Arabizi stayed popular after phones and browsers supported Arabic, because writers were used to it and more fluent on English keyboards [11].
Computational work on Arabizi has two tasks: finding it among English words, and converting it to Arabic script by transliterationRewriting a word from one script into another, sound by sound, without translating it.. Darwish labelled the words of tweets as Arabizi, English or other, reaching 98.5% word-level identification accuracy, and converted Arabizi to Arabic script with 88.7% accuracy, about a third of the remaining errors being spelling variants [11]. Bies and colleagues at the Linguistic Data Consortium built a parallel corpus of Egyptian SMS and chat, 2,140 conversations and 475,000 words, in which 66% of conversations were entirely in Arabizi, 15% entirely in Arabic script and 19% mixed [12]. Shazal, Usman and Habash replaced the separate steps with one sequence-to-sequence model that detects and transliterates together, reporting 80.6% word accuracy on a blind test set [13]. Two threads run through this work. Arabizi is inseparable from code-switching: the hard words are those valid in both languages, such as men, which is English, or Arabic for 'from' [11]. And the annotated data is not from the Gulf: the largest parallel resource is Egyptian [12], and we did not find a public, annotated corpus of Gulf Arabizi.
Code-switching
Hamed and colleagues' 2025 survey of code-switchingMoving between two languages or varieties within one conversation, sentence or even word, as in an English noun given an Arabic article. in Arabic language processing counts on average 12 papers a year since 2014, and finds the field concentrated on a few pairs: MSA with Egyptian Arabic for switching between varieties, and Egyptian-English and Algerian-French for switching between languages [14]. Gulf resources are recent and small. Mixat, published in 2024, is about 15 hours of speech from two Emirati podcasts whose hosts often switch between Emirati Arabic and English [6]. ZAEBUC-Spoken, also from 2024, is about twelve hours of recorded video meetings in which speakers role-play a work scenario, in MSA, Gulf and Egyptian Arabic and English, switching between them [15].
Surveys in the Gulf suggest the practice is ordinary. In Oman, Al-Qaysi and Al-Emran surveyed 338 students and 21 educators in higher education: 86.40% of the students and 81% of the educators said they code-switch on social networks, and WhatsApp was the application they used most for it [2]. In Saudi Arabia, Alsalami's questionnaire of 426 bilingual Saudis found that those with higher English proficiency switched more, and that working people used code-switching extensively [3]. In the UAE, Hopkyns, Zoghbor and Hassall found creative translanguaging, mixing Arabic and English, among 100 Emirati university students, where official policy presents the two as separate, pure languages [16]. All three are self-reports from educated, mostly young writers, and none measures business messages directly. Together they say that a customer who mixes Arabic and English is ordinary, not an edge case.
Gulf dialect resources and shared tasks
Three resources anchor Gulf dialect work in text. The Gumar corpus holds about 110 million words from 1,200 forum novels, labelled for sub-dialect document by document [5]; its coverage is uneven, with Saudi text 60.52% of the corpus and Omani text 1.13%. The MADAR corpus translates 2,000 travel sentences into 25 city dialects, four of them in the Gulf (Riyadh, Jeddah, Doha and Muscat), alongside a lexicon of 1,045 concepts [9]. CAMeL Tools, an open-source Python toolkit, bundles pre-processing, morphology, dialect identification, named entities and sentiment [17]; its dialect identifier distinguishes the same 25 cities plus MSA, so it knows Doha, Jeddah, Muscat and Riyadh but no Emirati, Kuwaiti or Bahraini city [18].
Shared tasks track how well dialects can be told apart. NADI 2020 covered 21 Arab countries and 100 provinces, using Twitter data [19]. By 2024 the organisers had moved to a multi-label task because dialects overlap so much that one sentence can be valid in several; but none of the nine country dialects in that year's multi-label test set came from a GCC state, and Emirati appeared only in the dialect-to-MSA translation subtask, where the winning system scored 23.38 BLEU on it [7]. NADI 2025, the first edition on speech, covered eight country dialects, of which only the UAE's came from the GCC [20]. Table 1 summarises what these resources cover.
| Resource | What it is | Gulf coverage |
|---|---|---|
| Gumar corpus (2016) | About 110 million words of forum novels, labelled by sub-dialect | Saudi 60.52%, Emirati 13.35%, Kuwaiti 5.91%, Omani 1.13%, Qatari 0.65%, Bahraini 0.49% of the corpus |
| MADAR corpus and lexicon (2018) | 2,000 travel sentences in 25 city dialects; 1,045-concept lexicon | Riyadh, Jeddah, Doha, Muscat |
| CODA guidelines (2018) | Conventional spelling for dialect words | 28 city dialects, Rabat to Muscat |
| Arabizi SMS and chat corpus (2014) | 475,000 words, Arabizi transliterated into Arabic script | None: Egyptian |
| NADI shared tasks (2020 to 2025) | Dialect identification, dialect-to-MSA translation, and from 2025 speech | 21 countries in 2020; in 2024 Emirati in translation only; in 2025 Emirati the only GCC dialect |
| Mixat (2024) | About 15 hours of podcast speech, transcribed | Emirati Arabic and Emirati-English |
| ZAEBUC-Spoken (2024) | About 12 hours of role-played work meetings | Gulf Arabic among MSA, Egyptian and English |
| SADA (2024) | 668 hours of Saudi television speech | Najdi, Hijazi and Khaleeji Saudi dialects |
| CAMeL Tools (2020) | Toolkit: normalisation, morphology, dialect identification, entities, sentiment | Dialect identifier covers Riyadh, Jeddah, Doha, Muscat |
Gumar percentages are from its Table 2, which also lists 10.03% mixed Gulf and 7.93% other Arabic, mostly MSA. SADA as described by Gerazov and colleagues (2025).
3Who writes the messages
A Gulf company's messages are written by its customers, suppliers and staff, and the region's demography shapes all three. Figure 1 shows non-nationals as a share of residents in mid-2022, as compiled from national statistical institutes by the Gulf Labour Markets, Migration and Population Programme [4]. Non-nationals were a majority in Qatar (87.9%), the UAE (87.1%), Kuwait (67.3%) and Bahrain (53.2%), and about four in ten residents of Saudi Arabia (41.6%) and Oman (40.1%); across the six states the share was 54.6%. The Qatar and UAE splits are the programme's own estimates, because neither state publishes totals by nationality. More recent official figures point the same way: Saudi Arabia's General Authority for Statistics put non-Saudis at 44.4% of the population in mid-2024 [21].
Each bar is the share of a country's residents who are not its citizens; the longer the bar, the more likely the writer of a message is not a national.
The non-national population is itself plural. India's Ministry of External Affairs counted 4,326,248 non-resident Indians in the UAE, 2,747,551 in Saudi Arabia, 1,036,389 in Kuwait, 830,491 in Qatar, 676,781 in Oman and 315,427 in Bahrain as of January 2026 [22], and Indian citizens are one group among many. Arabs from outside the Gulf write their own dialects, which the resources above treat as distinct from Khaleeji.
Two consequences follow. First, English works as the common language between people who share no other. Randall and Samimi described Dubai's reliance on expatriate labour as having made English the common language there [23], and Hopkyns and colleagues describe English as a shared language and the main medium of instruction in the UAE, while Emiratis mostly speak Khaleeji at home [16]. Second, Arabic itself is used by people who learned it at work. Linguists describe a Gulf Pidgin Arabic: a simplified contact variety that grew up between Arabic-speaking locals and migrant workers from India, Indonesia, Pakistan and the Philippines [24]. A storekeeper's message in that variety is Arabic, but not the Arabic a dialect model was trained on.
For an agent, this means the writer of a message cannot be inferred from the channel or the customer account. The same distributor may, in one morning, hear from a national buyer writing Gulf Arabizi, an accountant writing another Arab dialect, and a driver writing English with a few Arabic words.
4Anatomy of a business message
The message below is an illustrative example, written by us for this paper and not taken from any customer: a buyer at a catering company writes to a distributor's sales representative on WhatsApp. Every element in it is documented in the research above. We cannot say how often all of them occur together, because no public corpus of Gulf business messages exists. Table 2 annotates it word by word.
9ba7 il5air akhoy, il order رقم ٤٥٧٢ ma wesal. nabi 3 karateen ghee w 2 oil bachir before 8. pls confirm| Token | Script | Language | Meaning |
|---|---|---|---|
| 9ba7 | Latin letters and numerals | Gulf Arabic (Arabizi): 9 for ص, 7 for ح | 'Morning' |
| il5air | Latin letters and numerals | Gulf Arabic (Arabizi): 5 for خ | 'The good'; with the word before, 'good morning' |
| akhoy | Latin | Gulf Arabic (Arabizi) | 'My brother', a friendly form of address |
| il order | Latin | Arabic article on an English noun | 'The order' |
| رقم | Arabic | Arabic, shared with MSA | 'Number' |
| ٤٥٧٢ | Arabic-Indic digits | Numeral | 4572, the order number |
| ma wesal | Latin | Gulf Arabic (Arabizi) | 'Has not arrived' |
| nabi | Latin | Gulf Arabic (Arabizi) | 'We want', a Gulf form |
| 3 | Western digit | Numeral | Three, a quantity; here not the letter ع |
| karateen | Latin | Arabic plural of a borrowed word | 'Cartons' |
| ghee | Latin | English | Ghee |
| w | Latin | Arabic (Arabizi) | 'And' |
| 2 | Western digit | Numeral | Two, a quantity; here not the letter ء |
| oil | Latin | English | Cooking oil; brand and pack size unstated |
| bachir | Latin | Gulf Arabic (Arabizi): ch for a Gulf pronunciation of ك | 'Tomorrow' |
| before 8 | Latin and Western digit | English | A deadline; morning or evening is not said |
| pls confirm | Latin | English | 'Please confirm' |
Dialect spellings vary by writer; none is more correct than another.
Letters, numerals and numbers
The message uses numerals in two roles. In 9ba7 and il5air, as in Arabizi generally, a numeral stands for an Arabic letter [1, 11]. In '3 karateen' and '2 oil' the same characters are quantities. Palfreyman and Al Khalil recorded exactly this collision in the messages they collected [1]. A person resolves it from context without noticing. A parser that treats every digit as a number, or a transliterator that treats every 3 as ع, gets the order wrong.
Three digit systems and the direction of text
The order number is in Arabic-Indic digits and the quantities in Western ones. Unicode encodes two sets of Arabic digits separately: the Arabic-Indic digits used across most of the Arab world (U+0660 to U+0669), and the Eastern Arabic-Indic digits used for Persian, Urdu and Sindhi (U+06F0 to U+06F9), whose glyphs for four to seven vary between those languages [25]. A writer whose phone keyboard is set for Urdu or Persian types the second set, which looks like the first for several digits. With Western digits that makes three systems, sometimes inside one number, and they are distinct characters: they have to be mapped to one system before anything is compared with an ERP. Digits are also written left to right inside right-to-left text, and the Unicode Bidirectional Algorithm decides how a run of numbers and punctuation is displayed [26], so the order of characters on screen and in storage can differ. That matters when an agent copies a reference number back to a customer.
Dialect without a standard spelling
Words like bachir, akhoy and nabi have no MSA form to fall back on, and in Arabic script different writers spell them differently; the CODA guidelines exist because there is no standard [10]. A dictionary built on MSA fails on them, and a model that reads MSA well can still misread them. Identifying the dialect helps by telling the interpreter which conventions to expect, but the move to multi-label identification in 2024 is a reminder that a short message may be valid in several dialects at once [7].
English inside Arabic
'il order' puts the Arabic definite article on an English noun, and 'karateen' gives a borrowed word an Arabic plural. Bies and colleagues met the same pattern in Egyptian data, where English words take Arabic determiners [12]. For an agent the question is not which language the message is in, but what each word means. Labelling a whole message 'Arabic' or 'English' throws away the information it needs.
Voice notes
Some operational messages are not typed at all: they arrive as voice notes, in dialect, often with English mixed in. This is where the gap between technology built for MSA and the way the Gulf communicates is widest, as the next section shows.
5Where models built for MSA fall short
Where the evidence exists, it is direct. When Khalifa and colleagues hand-annotated about 4,000 words of Gulf Arabic from four Gumar novels, the MSA configuration of the MADAMIRA tagger met 9.3% out-of-vocabulary words against 5.6% for its Egyptian configuration, and was less accurate on every annotation layer (Table 3) [5]. Lemma accuracy, the layer an agent relies on to recognise a product or a verb whatever its form, was 64.03% with the MSA model and 77.02% with the Egyptian one. A model built for another dialect did better on Gulf text than a model built for the standard.
| Annotation layer | Built for MSA | Built for Egyptian Arabic |
|---|---|---|
| Conventional spelling | 83.81 | 88.34 |
| Full morphological analysis | 76.16 | 83.62 |
| Part of speech | 72.37 | 80.39 |
| Syntactic tag | 76.28 | 81.51 |
| Lemma | 64.03 | 77.02 |
Speech shows the gap at its widest. On Mixat, the Emirati-English podcast data, Whisper (medium) and MMS, used without fine-tuning, had word error rates of 195.98% and 188.1% on the Emirati Arabic segments, against 12.06% and 72.75% on the English ones (Figure 2) [6]. A word error rateThe share of words a speech recogniser gets wrong, counting substitutions, deletions and insertions; it can exceed 100% when a model adds many words that were never said. above 100% means the transcript holds more errors than the speech held words. The authors found that Whisper often produced a correct MSA translation instead of a transcript: it recognised the dialect but did the wrong task. ArTST, a model pre-trained on about a thousand hours of MSA, did best on the Emirati segments and still exceeded 100% [6]. Saudi speech behaves similarly: on the clean test set of SADA, 668 hours of Saudi television, Whisper medium without fine-tuning had a word error rate of 116.69%, and the best fine-tuned system 40.9% [27].
Each group is one kind of segment and each bar one model; lower is better, and above 100% the transcript contains more errors than the speech contained words.
With dialect data the picture changes. The best system in NADI 2025 reached 22.66% word error rate on Emirati test speech from another corpus [20]. The two Emirati numbers are not a controlled comparison, since the data differ, but they point the same way: dialect speech is workable with dialect data and close to unusable without it.
Large language models narrow the gap without closing it. The AraDiCE benchmarks found that Arabic-centred models such as Jais and AceGPT did better than multilingual models on dialect tasks, that models generally did better on MSA than on dialects, and that dialect identification, generation and translation remained hard [28]. On Arabizi, Al Almaoui, Bouillon and Hengchen tested seven models, including GPT-4o, Claude 3.5 Sonnet and Gemini 1.5 Pro, on Lebanese, Egyptian and Algerian Arabizi, and concluded that models struggle significantly with it; translations into English scored higher than translations into MSA [8]. No Gulf dialect was in their test, and AraDiCE's dialect benchmarks centre on Levantine and Egyptian, reporting Gulf results for only some tasks [28]. We did not find a published evaluation of current models on Gulf Arabizi or on Gulf business text.
Figure 3 condenses the evidence into a matrix: for each phenomenon in Table 2, whether a model trained mostly on MSA reads it well, and whether it needs dialect data, normalisation or both. It is our synthesis of the sources cited above, not a measurement.
Read across a row: the first column says whether a model trained mostly on MSA reads the phenomenon well, the next two whether it also needs dialect data or normalisation.
| Item | Read well by MSA-trained models | Needs dialect data | Needs normalisation |
|---|---|---|---|
| MSA in Arabic script | Yes | No | Partial |
| Gulf dialect in Arabic script | Partial | Yes | Partial |
| Arabizi: Latin letters and numerals | No | Yes | Yes |
| English words inside Arabic | Partial | Yes | Partial |
| Mixed digit systems | Partial | No | Yes |
| Gulf Pidgin Arabic | No | Yes | Partial |
| Voice notes in Gulf dialect | No | Yes | Partial |
6A reading pipeline for an agent
The pipeline in Figure 4 follows from the evidence. Its order matters: normalisation before detection, detection before interpretation, and confirmation before anything is written to a system that acts.
Follow the arrows from top to bottom; dashed arrows are the path taken when the reading is not certain.
- Normalise, and keep the original. Map the three digit systems to one, unify letter variants that writers use interchangeably (CAMeL Tools, for example, maps alef variants to bare alef, alef maksura to ya and ta marbuta to ha [29]) and collapse letters repeated for emphasis, a common feature of chat text [12]. Normalised text is for matching and search; the customer's own text is what is stored and quoted back.
- Detect script and dialect word by word. Darwish's word-level labelling of Arabizi against English is the model [11]: one message can need three labels, and dialect labels should allow more than one answer [7].
- Transliterate Arabizi into Arabic script, beside the original. Most Arabic dialect tools expect Arabic script, which is why the parallel Arabizi corpus was built [12], and one model can detect and transliterate in a single pass [13].
- Transcribe voice notes with a model adapted to the dialect, or send them to a person. Off-the-shelf recognisers fail on Gulf speech; adapted ones can work [6, 27, 20].
- Interpret only the fields that drive action: item, quantity, unit, date, time and reference number. Resolve digit-or-letter from context, and treat what is left unsaid, such as morning or evening, as unknown rather than guessed.
- Confirm in the customer's language, and ask rather than guess. Restate the facts that will be acted on, in the language and register the customer used, with numbers in one digit system, and ask one short question where something is ambiguous.
- Write the original, the reading and the reply to the record, so that a person can see later what was read, how it was read, and what the customer agreed to.
Step six carries most of the weight. As an illustrative example, a confirmation of the message in Table 2 would restate order 4572, three cartons of ghee and two of oil, tomorrow before 8, ask whether 8 means the morning, and do so in the customer's own mix of Arabic and English. Whether a company replies in Arabizi, in Arabic script or in English is a house-style decision for that company. The requirement is that the customer can answer in the form they already use, and is never translated into MSA or English in order to be understood.
7Evaluating on real-shaped data
Public benchmarks cannot tell a Gulf company how an agent will read its messages. The evaluation sets above are mostly Egyptian, Levantine or Maghrebi, the 2024 multi-label test set for written dialect identification contains no GCC dialect, and there is no public corpus of Gulf business messages [14, 28, 7, 8]. The evaluation set has to be built from the company's own past messages, and built so that the hard cases are visible.
- Stratify. Label each message by script (Arabic, Latin, mixed), language mix, dialect where it can be told, channel (text or voice) and kind of writer, and report results for each stratum. An average over a set dominated by clean Arabic script hides failure on Arabizi and voice notes.
- Score the fields, not the prose. What matters is whether the item, quantity, date and reference were read correctly, and whether the agent asked when it should have. Translation scores measure something else.
- Include the hard cases on purpose: digits that could be letters, deadlines without a morning or evening, English nouns with Arabic morphology, all three digit systems, and messages from writers for whom Arabic is a second language.
- Let the team that handles the messages decide what a correct reading is. They know that a given customer's 'oil' is a particular product and pack size; no annotator outside the company does.
- Re-run when the mix changes. A new customer segment, a new branch or more voice notes changes the strata, and the results with them.
These messages carry names, phone numbers and addresses. Building an evaluation set from them is processing personal data, and it should happen where the data already lives, under the company's own rules, rather than by sending samples abroad to be labelled.
8Implications for deployment
For a Gulf company putting agents into its operations, four things follow.
- Customers should not have to change how they write. An agent that asks for MSA or English, or answers only in MSA, moves the cost of the technology onto the customer. Arabizi and code-switching are documented Gulf practices, chosen for habit and identity as well as convenience [1, 2, 16].
- Ask for results on your own messages, by stratum. A general Arabic benchmark score says little about Gulf Arabizi, Gulf voice notes or pidgin Arabic.
- Treat voice notes as their own channel. Without recognition adapted to the dialect, they should go to a person.
- Put uncertain readings under a threshold. Where the agent is not sure what a message says, a person decides, as they would above any other limit.
This is how Qantara approaches the problem. Our engineers work on site inside the customer's operation and write down how the work really runs, including how its customers, suppliers and staff actually write. Agents work inside the systems the company already uses, such as WhatsApp, email and the ERP, and read and reply in Arabic and English on the same message. Before an agent goes live it is run on the company's past cases, with the company's own team marking what good looks like, and those cases are chosen to cover the scripts, dialects and channels the company really receives. Each message an agent reads, its reading and its reply are written to one operating record kept in the customer's country, and above the thresholds the customer sets, a person approves. We report no results here: this paper sets out the standard we hold that work to.
9Limitations
This paper is a review and an argument, not an experiment. We ran no models and built no corpus, so every performance number in it is someone else's, measured on someone else's data.
The Gulf-specific evidence is thin and uneven. The only Gulf Arabizi study we found dates from 2003 and rests on 79 survey responses and a small set of messages from one university [1]; the computational Arabizi work rests mostly on Egyptian data [12]. The code-switching surveys are self-reports from students, educators and educated adults, not measurements of business messages [2, 3]. The speech evidence is Emirati and Saudi [6, 27, 20], and Kuwaiti, Omani, Qatari and Bahraini speech may behave differently. The tagger results in Table 3 come from about 4,000 words of novels, not messages [5].
The population figures for Qatar and the UAE are estimates [4], and nationality is not language: a non-national may be a native speaker of Gulf Arabic, and a national may prefer English. The illustrative message is ours, and its mix is not a measured frequency; Figure 3 is a judgement. Models change quickly, and an evaluation of 2026 models on Gulf business text could narrow the gaps reported here. A public, consented corpus of Gulf business messages with results by stratum would test our argument directly, and would change it if models trained mainly on MSA read such messages as well as they read MSA.
10Conclusion
Gulf business messages are written in Gulf Arabic, in Arabizi, in English and in mixtures of all three, with numerals that may be letters and digits in more than one system, by writers of many nationalities, and sometimes not written at all but spoken. The research needed to read them exists in pieces: Arabizi transliteration, code-switching corpora, Gulf dialect corpora and toolkits, dialect identification. Each piece shows the same thing. Technology built for MSA loses accuracy on Gulf input, sometimes until the output is unusable, and public evaluation rarely looks like a Gulf business message.
An agent that acts on these messages should normalise without discarding the original, detect script and dialect word by word, interpret only the fields that drive action, and confirm in the customer's own language before it acts. It should be judged on the company's own messages, stratified so that the hard cases are visible. And it should never make a customer write in someone else's Arabic, or in English, in order to be understood.
References
- [1]Palfreyman, D. and Al Khalil, M. (2003). "A funky language for teenzz to use": Representing Gulf Arabic in instant messaging. Journal of Computer-Mediated Communication 9(1).
- [2]Al-Qaysi, N. and Al-Emran, M. (2017). Code-switching Usage in Social Media: A Case Study from Oman. International Journal of Information Technology and Language Studies 1(1).
- [3]Alsalami, A. I. (2021). Arabic English Code Switching among Saudi Speakers. Arab World English Journal 12(4), pp. 118-131.
- [4]Gulf Labour Markets, Migration and Population Programme, Gulf Research Center (2025). GCC: Total population and percentage of nationals and non-nationals in GCC countries (national statistics, mid-2022). Gulf Labour Markets, Migration and Population Programme. Accessed 14 October 2026.
- [5]Khalifa, S., Habash, N., Abdulrahim, D., et al. (2016). A Large Scale Corpus of Gulf Arabic. Proceedings of LREC 2016, pp. 4282-4289.
- [6]Al Ali, M. K. and Aldarmaki, H. (2024). Mixat: A Data Set of Bilingual Emirati-English Speech. Proceedings of SIGUL 2024 at LREC-COLING 2024, pp. 222-226.
- [7]Abdul-Mageed, M., Keleg, A., Elmadany, A., et al. (2024). NADI 2024: The Fifth Nuanced Arabic Dialect Identification Shared Task. Proceedings of the Second Arabic Natural Language Processing Conference (ArabicNLP 2024), pp. 709-728.
- [8]Al Almaoui, P., Bouillon, P. and Hengchen, S. (2025). Arabizi vs LLMs: Can the Genie Understand the Language of Aladdin?. Proceedings of Machine Translation Summit XX, Volume 2, pp. 28-41.
- [9]Bouamor, H., Habash, N., Salameh, M., et al. (2018). The MADAR Arabic Dialect Corpus and Lexicon. Proceedings of LREC 2018.
- [10]Habash, N., Eryani, F., Khalifa, S., et al. (2018). Unified Guidelines and Resources for Arabic Dialect Orthography. Proceedings of LREC 2018.
- [11]Darwish, K. (2014). Arabizi Detection and Conversion to Arabic. Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pp. 217-224.
- [12]Bies, A., Song, Z., Maamouri, M., et al. (2014). Transliteration of Arabizi into Arabic Orthography: Developing a Parallel Annotated Arabizi-Arabic Script SMS/Chat Corpus. Proceedings of the EMNLP 2014 Workshop on Arabic Natural Language Processing (ANLP), pp. 93-103.
- [13]Shazal, A., Usman, A. and Habash, N. (2020). A Unified Model for Arabizi Detection and Transliteration using Sequence-to-Sequence Models. Proceedings of the Fifth Arabic Natural Language Processing Workshop (WANLP 2020), pp. 167-177.
- [14]Hamed, I., Sabty, C., Abdennadher, S., et al. (2025). A Survey of Code-switched Arabic NLP: Progress, Challenges, and Future Directions. Proceedings of COLING 2025, pp. 4561-4585.
- [15]Hamed, I., Eryani, F., Palfreyman, D., et al. (2024). ZAEBUC-Spoken: A Multilingual Multidialectal Arabic-English Speech Corpus. Proceedings of LREC-COLING 2024, pp. 17770-17782.
- [16]Hopkyns, S., Zoghbor, W. and Hassall, P. J. (2021). The use of English and linguistic hybridity among Emirati millennials. World Englishes 40(2), pp. 176-190.
- [17]Obeid, O., Zalmout, N., Khalifa, S., et al. (2020). CAMeL Tools: An Open Source Python Toolkit for Arabic Natural Language Processing. Proceedings of LREC 2020, pp. 7022-7032.
- [18]CAMeL Lab, New York University Abu Dhabi (2026). camel_tools.dialectid: dialect identification. CAMeL Tools documentation. Accessed 14 October 2026.
- [19]Abdul-Mageed, M., Zhang, C., Bouamor, H., et al. (2020). NADI 2020: The First Nuanced Arabic Dialect Identification Shared Task. Proceedings of the Fifth Arabic Natural Language Processing Workshop (WANLP 2020), pp. 97-110.
- [20]Talafha, B., Toyin, H. O., Sullivan, P., et al. (2025). NADI 2025: The First Multidialectal Arabic Speech Processing Shared Task. Proceedings of the Third Arabic Natural Language Processing Conference: Shared Tasks (ArabicNLP 2025), pp. 720-733.
- [21]General Authority for Statistics, Kingdom of Saudi Arabia (2025). Population Estimates Publication 2024. General Authority for Statistics. Accessed 14 October 2026.
- [22]Ministry of External Affairs, Government of India (2026). Population of Overseas Indians. Ministry of External Affairs. Accessed 14 October 2026.
- [23]Randall, M. and Samimi, M. A. (2010). The status of English in Dubai. English Today 26(1), pp. 43-50.
- [24]Albaqawi, N. S. (2016). Unity and Diversity within Pidginized Arabic as Produced by Asian Migrant Workers in the Arabian Gulf. Journal of Second Language Teaching and Research 5(1).
- [25]The Unicode Consortium (2024). The Unicode Standard, Version 16.0.0, Chapter 9: Middle East-I. The Unicode Consortium.
- [26]Goregaokar, M. and Leroy, R. (2026). Unicode Standard Annex #9: Unicode Bidirectional Algorithm, revision 52. The Unicode Consortium. Accessed 14 October 2026.
- [27]Gerazov, B., Politi, M. and Bratières, S. (2025). Arabic ASR on the SADA Large-Scale Arabic Speech Corpus with Transformer-Based Models. arXiv:2508.12968.
- [28]Mousi, B., Durrani, N., Ahmad, F., et al. (2025). AraDiCE: Benchmarks for Dialectal and Cultural Capabilities in LLMs. Proceedings of COLING 2025, pp. 4186-4218.
- [29]CAMeL Lab, New York University Abu Dhabi (2026). camel_tools.utils.normalize: normalization. CAMeL Tools documentation. Accessed 14 October 2026.
Cite this paper
Qantara Research (2026). How the Gulf writes: Arabizi, code-switching and dialect in business messages. Qantara, 14 October 2026. https://qantara.systems/insights/how-the-gulf-writes
@techreport{qantara2026howthegulfwrites,
title = {How the Gulf writes: Arabizi, code-switching and dialect in business messages},
author = {Qantara Research},
institution = {Qantara},
year = {2026},
month = {oct},
url = {https://qantara.systems/insights/how-the-gulf-writes}
}Disclosure
Every number in this paper is quoted from the cited sources; we ran no models and computed no new figures. The message in Table 2 and its annotation, the confirmation described in the pipeline section, the matrix in Figure 3 and the pipeline in Figure 4 are ours, and the message and confirmation are illustrative. Qantara's approach is described as practice, not as results.
