# Arabic (Egyptian dialect) veterinary diagnosis assistant: dialect -> clinical-English gap and low accuracy on real owner messages

> Source: <https://discuss.huggingface.co/t/arabic-egyptian-dialect-veterinary-diagnosis-assistant-dialect-clinical-english-gap-and-low-accuracy-on-real-owner-messages/190643#post_2>
> Published: 2026-10-16 00:43:21+00:00

I’m a solo founder building an Arabic veterinary diagnosis assistant for livestock and pets in Egypt (poultry, cattle, buffalo, horses, cats, dogs).

**Setup:** ~216 English vet textbooks → 530k chunks (Postgres + pgvector, paraphrase-multilingual-mpnet 768-d with a 128-token window, plus lexical search); Claude Haiku as the LLM.

**What we measured:**

- Retrieval-first (embed the Arabic symptoms → top-10 chunks → LLM) is weak.
- Given the correct passage, the LLM gets ~92% top-1, so the reasoning works.
- “LLM proposes 10 candidate diseases → search the books by disease name → LLM picks top-3” reaches 92% top-3 on clean Arabic (MSA) case descriptions.
- On real owner messages from Arabic Q&A sites, agreement is only ~40% exact and ~72% exact or related (labels are the replier’s answers, not vet-reviewed).

**The translation/dialect problem:** owners write in Egyptian colloquial Arabic (“الفراخ بتنهج وفاتحة بوقها”, “الجاموسة مانعة الأكل”), while all the evidence is English and organised by disease name. What we tried:

1. AGROVOC Arabic–English glossary: +1 case, and generic terms (“heat”, “symptoms”) diluted queries.
2. A 287-term Egyptian field-term dictionary: fixed 3 cases, broke 3.
3. A 410-term controlled clinical vocabulary for poultry (MSA + Egyptian): did not meet our gate.
4. LLM rewriting of the whole question into English clinical signs before diagnosis: 38.3% vs 40.4% without, so no gain.
5. BGE-M3 instead of mpnet: not better on our pilot, and 7x slower.

**Planned next:** self-consistency over candidate lists, 2–3 follow-up questions to separate the top-3, a canonical disease list with hierarchical scoring, and a small paid vet audit.

**Questions:**

1. What has worked for you mapping dialectal or lay symptom text to clinical concepts? Is the right move to normalise the dialect, or to ask follow-up questions?
2. Do you know Egyptian/Arabic medical NLP resources (dialect symptom lexicons, annotated corpora) or veterinary ontologies (VeNom, SNOMED vet) that can be used?
3. Any pitfalls with LLM-simulated owners for evaluation?

Thanks!

 
 
I think the biggest gap is embedding Egyptian Arabic symptoms straight against English textbook chunks. I’d try a step before retrieval: have the LLM rewrite the owner’s message into a short clinical English symptom list, then retrieve on that. 128 tokens is also pretty tight for textbook text, I’d test 300-500 side by side on your eval set. keeping each chunking + model variant as its own index on the same table makes those comparisons cheap: [github.com/pixeltable/pixeltable/blob/main/docs/release/platform/embedding-indexes.ipynb](https://github.com/pixeltable/pixeltable/blob/main/docs/release/platform/embedding-indexes.ipynb)