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Freedom251 › Blog › Arabizi

How to Build and Evaluate a Mixed-Language WhatsApp Test Set for Gulf Businesses

By Freedom251 Team · · 10 min read

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A reliable WhatsApp assistant for a Gulf business must handle Arabic script, English, Arabizi (Arabic written with Latin characters and numerals), Gulf dialect, code-switching, voice notes and terse replies whose meaning depends on earlier messages. Build the evaluation set from several hundred anonymised messages from the company’s own WhatsApp inbox, label the expected action for every case, and make routing and safety as important as text-answer accuracy. Re-run the same set after prompt, model and message-template changes so regressions are visible before customers find them.

What a mixed-language test set must prove

A test set is a controlled collection of realistic customer messages with a known, reviewable outcome. For a WhatsApp assistant, that outcome is not always a sentence. It may be a booking, a correctly extracted date, a request for missing information, a refusal, or an immediate transfer to a human agent.

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The set should answer five separate questions:

  • Language handling: Did the system identify Arabic script, English, Arabizi, dialect and language switches within one message?
  • Task correctness: Did it classify the intent, extract dates, numbers and locations, and apply the business policy?
  • Conversation safety: Did it recognise uncertainty, avoid inventing an answer and preserve context during escalation?
  • Speech performance: Was a voice note transcribed accurately, and did the assistant interpret that transcript correctly?
  • Operational stability: Did performance hold after prompts, model versions or WhatsApp templates changed?

Keep these dimensions separate in the report. A correct intent reached through a bad transcription is a speech failure, not a language-understanding success. Conversely, a perfect transcript followed by an unsafe answer is an assistant failure.

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Why Gulf language coverage needs deliberate sampling

Public datasets show why a Gulf-focused set cannot be replaced by an English-only or Modern Standard Arabic benchmark. The MBZUAI Mixat project (2024) reports 15 hours of Emirati-English speech, 5,307 sentences and code-switching in 36% of sentences. Its documentation recommends separate training and testing splits and identifies a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 licence. The UAE University publication record describes shortcomings in pretrained Arabic and multilingual speech-recognition systems on low-resource Emirati dialect and the added difficulty of recognising code-switching.

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Written and spoken coverage can be supplemented by ZAEBUC (2024), which includes Modern Standard Arabic, Gulf Arabic and Egyptian Arabic. Its site reports 930 code-switched spoken utterances and provides guidance on conversational speech, code-switching and orthography. These corpora are useful for designing categories and edge cases; they are not a substitute for your own customers’ vocabulary, policies or local place names.

No authoritative statistic establishes what share of Gulf-business WhatsApp traffic is mixed-language, and there is no universal accuracy threshold that guarantees a safe deployment. Use the business’s own inbox as the baseline, publish slice counts and, where sample sizes permit, confidence intervals.

Collect and govern the source messages

Sample the business’s real conversations

Start with several hundred anonymised messages sampled across the inbox rather than a hand-picked list of unusually difficult examples. Include routine requests, failed conversations, successful human handoffs, voice notes and messages from different operating hours or locations. Preserve enough preceding turns for short replies such as “yes,” “tomorrow,” or “same one” to be interpretable.

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Remove names, phone numbers, order IDs, addresses and other direct identifiers. Replace them with stable tokens so the same entity can be tested consistently without exposing a customer. Keep the original language form, spelling variation and punctuation unless those details themselves reveal identity.

Document consent and retention

Before launch, record the WhatsApp opt-in basis, who can access the dataset, where processing occurs, and how long raw audio and transcripts are retained. The implementation guidance for UAE businesses recommends using the WhatsApp Business Platform, per-message language detection, treating Arabizi as Arabic, preserving numerals, matching the reply language to the customer, explicit stop conditions and weekly scoring of anonymised bilingual conversations. Adapt those controls to the business’s legal and contractual obligations rather than assuming that a regional deployment has one universal rule.

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Use a label schema that captures the expected action

Give every case a stable ID and store labels in a version-controlled file or database. A practical record contains:

Field What to record
Case ID Permanent identifier such as KW-RET-0042; do not recycle IDs when the text changes.
Conversation context The minimum prior turns needed to interpret the message, with sensitive values anonymised.
Message or audio reference Text for typed messages; a protected file reference for voice notes.
Language spans English, Arabic script, Arabizi and any mixed spans; mark uncertainty rather than forcing one label.
Dialect or register For example Gulf Arabic, Modern Standard Arabic, Egyptian Arabic, colloquial Emirati or unknown.
Intent The business taxonomy label, such as delivery status, appointment change or refund request.
Entities Dates, times, quantities, currencies, order numbers, locations and product names, with normalized and original forms.
Expected action Answer, ask a clarification, perform a permitted operation, refuse, or route_to_person.
Policy notes Authentication needed, prohibited disclosure, eligibility rule or other constraint.
Reference response Required facts and language; avoid demanding one exact wording if several answers are safe.
Difficulty and notes Reason for inclusion, ambiguity, spelling variant, code switch or known transcription issue.

Keep the expected action distinct from the expected wording. This lets reviewers give credit for a concise, natural answer while still detecting a wrong booking, missing warning or unsafe disclosure.

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Build balanced language and spelling slices

Arabic script, English and Arabizi

Tag the language of each span, not only the whole message. Include fully Arabic messages, fully English messages and combinations such as an Arabic request with an English product name. Arabizi deserves its own slice because Latin letters and numerals can represent Arabic sounds inconsistently. Include common alternatives, omitted vowels, doubled letters, punctuation-free text and spelling that changes across customers.

Test the policy recommended for the deployment: detect language per message, classify Arabizi as Arabic for routing and reply in the customer’s language unless the customer asks to switch. A message containing English terms should not automatically trigger an English-only response.

Dialect and register

Separate Gulf colloquial forms from Modern Standard Arabic where the distinction affects intent or politeness. Add locally used service terms, abbreviations and transliterations. If the business serves more than one Gulf market, tag the market and place names so a spelling familiar in one country is not silently normalised to another.

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Code-switch position and density

Do not limit mixed cases to one English noun inside an Arabic sentence. Include switches at the beginning, middle and end; alternating clauses; product names, dates and addresses in different scripts; and messages that begin in one language and end with a request in another. Record the number and location of language spans so results can be compared across builds.

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Numbers and place names

Pair Arabic-Indic digits with Western digits for the same task. Test dates with different separators, month names in Arabic and English, 12-hour and 24-hour times, decimal commas and currency abbreviations. Add alternative spellings of cities, districts, malls and landmarks. The expected label should preserve the customer’s original value and include a normalized value only when the business system requires it.

Cover intent, extraction and policy together

A language label alone cannot show whether the assistant is useful. For each major intent, create matched variants that change language, script, politeness, spelling and context while keeping the requested action constant. Include near-neighbours that require different actions: a delivery-status question versus a cancellation request, a general price query versus a request for a restricted discount, or a change to an authenticated booking versus an unauthenticated request.

Mark every entity that must be extracted and define acceptable normalization. A date may be accepted only if the day-month interpretation is unambiguous; a location may require confirmation when two branches share a name. Add cases with missing, conflicting or impossible values to test clarification and refusal behavior.

Short replies and context dependence

Place replies such as “ok,” “لا,” “بعد شوي” (later), “same,” and “the second one” after different preceding questions. The same token can confirm, reject or select an option depending on the prior turn. Score whether the assistant used the right context, not merely whether it recognized the word.

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Safety and escalation cases

Include cases where the correct label is route_to_person: unresolved ambiguity, complaints, payment disputes, requests for sensitive account information, policy exceptions and explicit requests for a human. If language or intent confidence falls below the business threshold, the expected action should also be a handoff. The handoff payload should preserve the conversation, detected languages, extracted entities and the reason for escalation so the agent does not make the customer repeat the issue.

As Ujjwal Dubey puts it: “A wrong confident reply in the wrong language costs more than a short wait.” Treat that as a design principle: uncertainty should change the action, not just lower a score.

Test voice notes as a two-stage system

Voice notes are a separate modality. Preserve representative WhatsApp audio with permission, and annotate language spans, transcription, entities, intent and expected action. Keep the audio reference linked to its transcript version so a transcription correction does not erase the original evaluation.

  1. Evaluate speech recognition: Compare the transcript with a human-reviewed reference, paying attention to Emirati pronunciation, code-switch boundaries, names, numbers and noisy recordings.
  2. Evaluate understanding: Feed the approved transcript to the assistant and score intent, entities, policy and language of the reply.
  3. Evaluate the pipeline: Run the original audio end to end and record where errors enter. Report speech, understanding and combined results separately.

This separation prevents a poor transcript from being hidden inside one end-to-end percentage. The UAE implementation guidance recommends transcription when message volume justifies it; make that decision using observed volume, latency, cost and the consequence of errors.

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Define metrics that reflect customer risk

Report results by slice and by business consequence. A single overall accuracy can improve while a critical language or handoff path gets worse.

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Metric How to use it
Intent accuracy or macro-F1 Calculate per intent and average across intents so common requests do not hide rare failures.
Language-span accuracy Measure script, Arabizi and intra-message switch detection separately.
Entity precision, recall and exact-match rate Report dates, numbers, locations and identifiers independently; a wrong value can be more serious than a missed one.
Safe-handoff recall Of all cases labelled route_to_person, how many actually reached a person?
Unsafe confident-response rate Count cases where the assistant answered instead of escalating despite a required handoff or low confidence.
Language-appropriate reply rate Check whether the response follows the customer’s language and script policy.
Voice transcription quality Use a speech metric suitable for the language, then pair it with downstream intent and entity scores.
Regression delta Compare the same cases before and after every prompt, model or template change.

Always publish denominators or slice counts. For a small slice, label the result as directional rather than treating a few examples as a stable estimate. Review false positives and false negatives manually, especially when the system appeared confident.

Run the set as a repeatable release gate

  1. Freeze a version: Store the case records, label definitions, evaluator version and anonymisation rules in the repository. Keep a locked test partition that is not used to tune prompts.
  2. Validate labels: Have bilingual reviewers resolve disagreements and record why an expected action changed. Use adjudication for dialect, Arabizi and ambiguous context.
  3. Run automated evaluation: Capture the model version, prompt, retrieval data, temperature or equivalent settings, template version and timestamp.
  4. Run safety checks: Verify every required handoff, refusal, authentication step and stop condition. A response that sounds fluent but violates policy is a failure.
  5. Inspect slices: Break out Arabic script, Arabizi, English, mixed spans, numeral formats, place-name variants, voice notes and context-dependent replies.
  6. Review errors: Store the input, output, expected action, confidence, failure category and reviewer note. Do not report only the cases handled correctly.
  7. Approve or block release: Set organization-specific thresholds for critical slices and require sign-off when a safety or handoff metric regresses.

Run the complete set before launch and after prompt edits, model changes and template changes. Add newly observed failure patterns as regression cases, while preserving the locked partition so improvements are measurable rather than tailored to a moving target.

Design bilingual replies and handoffs

A correct answer can still fail if it is delivered in an unsuitable language or hides uncertainty. Define response rules in the labels: Arabic script when the customer writes Arabic script, Arabic-friendly handling for Arabizi, English when the customer writes English, and a deliberate bilingual format only when the customer requests it or the workflow requires it. Do not translate names, order identifiers or numeric values in ways that could alter them.

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For a handoff, send the agent the original message, the relevant preceding turns, detected language spans, transcript if applicable, extracted entities and the escalation reason. Tell the customer what will happen next without promising a response time that the operation cannot meet. Stop automated replies after the handoff unless the agent or policy explicitly returns the conversation to automation.

Common failure modes and fixes

  • English-only sampling: Add matched Arabic-script, Arabizi and mixed variants for every high-volume intent.
  • One label per message: Annotate language spans and code-switch positions.
  • Ignoring Arabizi: Maintain an Arabizi slice and test inconsistent numerals and spellings.
  • Scoring only final text: Score routing, entities, policy and handoff separately.
  • Testing voice notes as text: Preserve audio and evaluate transcription before understanding.
  • Losing context: Include prior turns for short replies and verify the handoff transcript.
  • Overfitting to a benchmark: Sample the business’s own inbox and retain a locked test partition.
  • Reporting one percentage: Publish slice counts, critical-error examples and regression deltas.
  • Changing labels silently: Version the taxonomy and document adjudication decisions.

A practical launch checklist

  • Several hundred anonymised messages represent real customers, intents and operating conditions.
  • Arabic script, English, Arabizi, Gulf dialect, code-switching and spelling variants are labelled.
  • Arabic-Indic and Western numerals, dates, times, currencies and local place-name variants are present.
  • Context-dependent short replies and multi-turn conversations are included.
  • Voice notes have protected audio references plus reviewed transcripts and entity labels.
  • Every case has an expected action, including explicit route_to_person outcomes.
  • Low-confidence and policy-sensitive cases have defined escalation behavior.
  • Metrics distinguish language, intent, extraction, speech, safety and operational regression.
  • Opt-in, access, processing location and retention rules are documented.
  • The versioned set runs before launch and after prompt, model and template changes.
  • Weekly anonymised bilingual-conversation scoring feeds new regression cases.

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