Muhammad AbdulMageed

Canada Research Chair in Natural Language Processing and Machine Learning; Director of @UBC Deep Learning & NLP Group.

[Muhammad AbdulMageed @mageed]

Honored to share that I’ve received the Abdul Hameed Shoman Award for Arab Researchers (2025) in the AI and Arabic Language category.

This recognition reflects the sustained work of my students, collaborators, and mentors—thank you. I’m grateful for the chance to advance Arabic AI/NLP in service of our communities.

About the award: Established in 1982 by the Abdul Hameed Shoman Foundation—the Arab Bank’s cultural and social responsibility arm—the award recognizes outstanding scientific contributions by Arab researchers across disciplines.

#AI #ArabicNLP #NLP #LLMs #Research #ArabWorld

[Muhammad AbdulMageed @mageed]

🔗 https://t.co/gIqe0i72jH

[Muhammad AbdulMageed @mageed]

We’re organizing the NADI shared task for the seventh year in a row. NADI 2026 will focus on speech technology, with a wide range of tasks. Tasks include real-world automatic speech recognition, speech dialect identification, text-to-speech, and speech translation. Join us!

[Muhammad AbdulMageed @mageed]

Link: https://t.co/iixfx1KZOy

[Muhammad AbdulMageed @mageed]

I spent the morning today helping my students on a new work on African speech and language technology. We talk about a lot of datasets and tools, and present a lot of numbers. But I didn’t see language names. I saw people.

With each new language we add, I saw the people we can help.

I thought about the patient whose life we can help save by making health information more accessible. The farmer whose crop we can help protect through timely advice. The teacher we can help reach more students. The parent we can help support, the child we can help teach, and the community worker we can help empower.

Here’s a post I wrote several months ago. We have done a lot of work since then, but it delivers my own passion for this social program.

[ArabicNLP2026 @_ArabicNLP]

📢 مؤتمر ArabicNLP 2026 - الدعوة الثالثة لتقديم الأوراق البحثية 📢
سيعقد المؤتمر الرابع لمعالجة اللغة العربية الطبيعية (ArabicNLP 2026) بالتزامن مع مؤتمر EMNLP 2026 في بودابست، المجر (أكتوبر 2026).

- آخر موعد لتقديم الملخصات: ١٨ يونيو ٢٠٢٦
- آخر موعد لتقديم الأبحاث الكاملة: ٢٥ يونيو ٢٠٢٦

للاطلاع على المزيد من التفاصيل حول تعليمات التقديم وغيرها، تفضل بزيارة الموقع الإلكتروني:https://t.co/jeWMtQgp0R

📢 ArabicNLP 2026 - Third Call for Papers 📢
𝐓𝐡𝐞 𝐅𝐨𝐮𝐫𝐭𝐡 𝐀𝐫𝐚𝐛𝐢𝐜 𝐍𝐚𝐭𝐮𝐫𝐚𝐥 𝐋𝐚𝐧𝐠𝐮𝐚𝐠𝐞 𝐏𝐫𝐨𝐜𝐞𝐬𝐬𝐢𝐧𝐠 𝐂𝐨𝐧𝐟𝐞𝐫𝐞𝐧𝐜𝐞 (𝐀𝐫𝐚𝐛𝐢𝐜𝐍𝐋𝐏 𝟐𝟎𝟐𝟔) 𝐰𝐢𝐥𝐥 𝐛𝐞 𝐜𝐨-𝐥𝐨𝐜𝐚𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐄𝐌𝐍𝐋𝐏 𝟐𝟎𝟐𝟔 𝐢𝐧 𝐁𝐮𝐝𝐚𝐩𝐞𝐬𝐭, 𝐇𝐮𝐧𝐠𝐚𝐫𝐲 (𝐎𝐜𝐭𝐨𝐛𝐞𝐫 𝟐𝟎𝟐𝟔)
- Abstract Deadline: June 18, 2026
- Full Paper Submission: June 25th, 2026

Visit the website for more details on submission instructions and more: https:https://t.co/jeWMtQgp0R

#ArabicNLP2026⁩
#ArabicNLP⁩

[ISED @ISED_CA]

Minister Joly announces funding for Talent Innovation Canada, a new organization that will connect top graduate talent with Canadian companies working to solve R&D challenges.

Learn more: https://t.co/gFLxhz9odJ

[Muhammad AbdulMageed @mageed]

@UBCNews @UBC_Arts @UBCLinguistics @UBCiSchool @UBC_CS @UBC_NLP

[Muhammad AbdulMageed @mageed]

𝗧𝗵𝗲 𝗻𝗲𝘅𝘁 𝗳𝗿𝗼𝗻𝘁𝗶𝗲𝗿 𝗶𝗻 𝗔𝗜 𝗶𝘀 𝗻𝗼𝘁 𝗼𝗻𝗹𝘆 𝘁𝗲𝗮𝗰𝗵𝗶𝗻𝗴 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘁𝗼 𝘀𝗽𝗲𝗮𝗸 𝗺𝗼𝗿𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀.

It is teaching them to stop flattening people into language names.

“Arabic.”
“English.”
“Spanish.”
...

These labels are convenient for datasets.

But humans do not speak labels.

We speak from cities, villages, families, histories, professions, memories, migrations, jokes, wounds, rituals, and relationships.

A grandmother does not speak like a government form.

A patient does not describe pain like a Wikipedia article.

A farmer, a nurse, a student, a shopkeeper, a traveler, and a child do not all speak the same “standard” version of a language.

And yet, much of modern AI still behaves as if language were uniform, and separable from the people who use it.

This is one of the great unsolved problems in multilingual AI.

Not only translation.

𝗩𝗮𝗿𝗶𝗮𝘁𝗶𝗼𝗻.
𝗦𝗼𝗰𝗶𝗮𝗹 𝗺𝗲𝗮𝗻𝗶𝗻𝗴.
𝗗𝗶𝗮𝗹𝗲𝗰𝘁.
𝗥𝗲𝗴𝗶𝘀𝘁𝗲𝗿.
𝗖𝘂𝗹𝘁𝘂𝗿𝗲.
𝗖𝗼𝗻𝘁𝗲𝘅𝘁.
𝗪𝗵𝗼 𝗴𝗲𝘁𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗼𝗼𝗱 𝗯𝘆 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀, 𝗮𝗻𝗱 𝘄𝗵𝗼 𝗴𝗲𝘁𝘀 𝗻𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗲𝗱 𝗮𝘄𝗮𝘆.

This is the global problem behind our new project:
𝗔𝗹𝗲𝘅𝗮𝗻𝗱𝗿𝗶𝗮.

The name is intentional.

Alexandria was never just a city.

It was one of humanity’s boldest symbols of knowledge moving across languages, cultures, and worlds.

A place where translation was not clerical work.
It was civilization-building.

Today, we borrow that name for a new question at the heart of AI:

𝗖𝗮𝗻 𝗺𝗮𝗰𝗵𝗶𝗻𝗲𝘀 𝘂𝗻𝗱𝗲𝗿𝘀𝘁𝗮𝗻𝗱 𝗔𝗿𝗮𝗯𝗶𝗰 𝗻𝗼𝘁 𝗮𝘀 𝗼𝗻𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲, 𝗯𝘂𝘁 𝗮𝘀 𝗮 𝗹𝗶𝘃𝗶𝗻𝗴 𝘄𝗼𝗿𝗹𝗱 𝗼𝗳 𝘃𝗼𝗶𝗰𝗲𝘀?

For too long, Arabic has been flattened in technology.

One label: “Arabic.”
One assumed standard.
One imagined speaker.
One simplified linguistic reality.

But Arabic is not one voice.

Arabic is Cairo and Casablanca.

Amman and Muscat.
Khartoum and Tunis.
Beirut and Riyadh.
Sana’a, Tripoli, Nouakchott, Gaza, Damascus, Algiers, Doha, Manama, and beyond.

It is dialect.
It is register.
It is gendered interaction.
It is code-switching.
It is place, intimacy, politeness, humor, memory, identity, and everyday life.

So I am thrilled to share that 𝗔𝗹𝗲𝘅𝗮𝗻𝗱𝗿𝗶𝗮 has been accepted to 𝗔𝗖𝗟 𝟮𝟬𝟮𝟲 𝗠𝗮𝗶𝗻.

𝗔𝗹𝗲𝘅𝗮𝗻𝗱𝗿𝗶𝗮: 𝗔 𝗠𝘂𝗹𝘁𝗶-𝗗𝗼𝗺𝗮𝗶𝗻 𝗗𝗶𝗮𝗹𝗲𝗰𝘁𝗮𝗹 𝗔𝗿𝗮𝗯𝗶𝗰 𝗠𝗮𝗰𝗵𝗶𝗻𝗲 𝗧𝗿𝗮𝗻𝘀𝗹𝗮𝘁𝗶𝗼𝗻 𝗗𝗮𝘁𝗮𝘀𝗲𝘁 𝗳𝗼𝗿 𝗖𝘂𝗹𝘁𝘂𝗿𝗮𝗹𝗹𝘆 𝗜𝗻𝗰𝗹𝘂𝘀𝗶𝘃𝗲 𝗮𝗻𝗱 𝗟𝗶𝗻𝗴𝘂𝗶𝘀𝘁𝗶𝗰𝗮𝗹𝗹𝘆 𝗗𝗶𝘃𝗲𝗿𝘀𝗲 𝗟𝗟𝗠𝘀

Alexandria is our largest community-built Arabic machine translation project so far: a large-scale, human-translated, multi-domain dataset designed to help AI systems translate Arabic as people actually use it.

It brings together:
📷 𝟭𝟯 𝗔𝗿𝗮𝗯 𝗰𝗼𝘂𝗻𝘁𝗿𝗶𝗲𝘀
📷 𝟯𝟰𝗞+ 𝗺𝘂𝗹𝘁𝗶-𝘁𝘂𝗿𝗻 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝘀
📷 𝟭𝟬𝟳𝗞+ 𝗘𝗻𝗴𝗹𝗶𝘀𝗵 📷 𝗱𝗶𝗮𝗹𝗲𝗰𝘁𝗮𝗹 𝗔𝗿𝗮𝗯𝗶𝗰 𝗽𝗮𝗿𝗮𝗹𝗹𝗲𝗹 𝘁𝘂𝗿𝗻𝘀
📷 𝟭𝟭 𝘀𝗼𝗰𝗶𝗮𝗹-𝗶𝗺𝗽𝗮𝗰𝘁 𝗱𝗼𝗺𝗮𝗶𝗻𝘀, including healthcare, education, agriculture, legal/financial services, tourism, logistics, and workplace communication
📷 𝗳𝗶𝗻𝗲-𝗴𝗿𝗮𝗶𝗻𝗲𝗱 𝗰𝗶𝘁𝘆-𝗹𝗲𝘃𝗲𝗹 𝗱𝗶𝗮𝗹𝗲𝗰𝘁 𝗺𝗲𝘁𝗮𝗱𝗮𝘁𝗮
📷 𝘀𝗽𝗲𝗮𝗸𝗲𝗿–𝗮𝗱𝗱𝗿𝗲𝘀𝘀𝗲𝗲 𝗴𝗲𝗻𝗱𝗲𝗿 𝗰𝗼𝗻𝗳𝗶𝗴𝘂𝗿𝗮𝘁𝗶𝗼𝗻𝘀
📷 𝗺𝘂𝗹𝘁𝗶-𝘁𝘂𝗿𝗻 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗰𝗼𝗻𝘁𝗲𝘅𝘁
📷 𝗻𝗮𝘁𝘂𝗿𝗮𝗹𝗹𝘆 𝗼𝗰𝗰𝘂𝗿𝗿𝗶𝗻𝗴 𝗰𝗼𝗱𝗲-𝘀𝘄𝗶𝘁𝗰𝗵𝗶𝗻𝗴 𝘄𝗶𝘁𝗵 𝗘𝗻𝗴𝗹𝗶𝘀𝗵, 𝗙𝗿𝗲𝗻𝗰𝗵, 𝗮𝗻𝗱 𝗠𝗦𝗔
📷 𝗲𝘃𝗮𝗹𝘂𝗮𝘁𝗶𝗼𝗻 𝗰𝗼𝗱𝗲 𝗮𝗻𝗱 𝗱𝗮𝘁𝗮 𝗰𝗿𝗲𝗮𝘁𝗶𝗼𝗻 𝗴𝘂𝗶𝗱𝗲𝗹𝗶𝗻𝗲𝘀 to support reproducibility and future community work

But Alexandria is not only a dataset.

It is a refusal.

A refusal to treat dialects as noise.

A refusal to treat “standard” language as the only legitimate language.

A refusal to build AI systems that understand the center and mishear the margins.

Dialects are not broken versions of languages.
They are knowledge systems.

They carry social meaning.
They carry identity.
They carry memory.
They carry belonging.
They carry the subtle difference between being translated and being understood.

When AI erases dialects, it does not merely make a linguistic mistake.

It erases context.
It erases communities.
It erases the conditions under which language becomes human.

Our findings show a clear gap: current LLMs can often preserve meaning reasonably well, but still struggle to generate fluent, locally authentic dialectal Arabic, especially across fine-grained sub-dialects, domains, and social contexts.

That gap matters.

It matters when a patient describes symptoms in the words that feel natural to them.

It matters when a farmer asks for advice.
It matters when a student learns.
It matters when a traveler seeks help.
It matters when public services are supposed to reach everyone, not only those who speak the “right” variety.

Alexandria is our attempt to say something plainly:

𝗧𝗵𝗲 𝗳𝘂𝘁𝘂𝗿𝗲 𝗼𝗳 𝗺𝘂𝗹𝘁𝗶𝗹𝗶𝗻𝗴𝘂𝗮𝗹 𝗔𝗜 𝗰𝗮𝗻𝗻𝗼𝘁 𝗯𝗲 𝗯𝘂𝗶𝗹𝘁 𝗯𝘆 𝗮𝘃𝗲𝗿𝗮𝗴𝗶𝗻𝗴 𝗵𝘂𝗺𝗮𝗻𝗶𝘁𝘆 𝗶𝗻𝘁𝗼 𝘀𝘁𝗮𝗻𝗱𝗮𝗿𝗱 𝗳𝗼𝗿𝗺𝘀.

We need systems that can handle variation, not erase it.
Systems that can model culture, not smooth it away.
Systems that understand that linguistic diversity is not a complication to be removed.

It is the world.

This is true for Arabic.

It is also true globally, for languages with dialects, accents, registers, contact varieties, mixed codes, regional forms, Indigenous varieties, diasporic forms, youth speech, professional speech, and deeply situated cultural meanings.

In the old Alexandria, knowledge moved across languages.

With this new Alexandria, we hope to help AI move across dialects, communities, and lived realities.
This project was only possible because of a remarkable community of collaborators across the Arab world. I am deeply grateful to everyone who contributed to building, translating, validating, evaluating, and shaping this resource.

Paper: https://t.co/HRmzatsYTm
Project website: https://t.co/ceAvzX6GI6
Dataset: https://t.co/3wsnLalVPC
Code and guidelines: https://t.co/QhlDZuKS4X

𝗔𝗜 𝘀𝗵𝗼𝘂𝗹𝗱 𝗻𝗼𝘁 𝗼𝗻𝗹𝘆 𝗹𝗲𝗮𝗿𝗻 𝗺𝗼𝗿𝗲 𝗹𝗮𝗻𝗴𝘂𝗮𝗴𝗲𝘀.
𝗜𝘁 𝘀𝗵𝗼𝘂𝗹𝗱 𝗹𝗲𝗮𝗿𝗻 𝘁𝗼 𝗵𝗲𝗮𝗿 𝗽𝗲𝗼𝗽𝗹𝗲.

[ArabicNLP2026 @_ArabicNLP]

Great news for the Arabic NLP community! 🎉

📢 Call for Shared Task Proposals is now open!

الإعلان الأول للدعوة لتقديم (Shared Tasks) ضمن المؤتمر الرابع للمعالجة الآلية للغة العربية ArabicNLP 2026، والذي سيُعقد بالتزامن مع EMNLP 2026 في بودابست، هنغاريا، خلال الفترة 24–29 أكتوبر 2026.

🗓️ آخر موعد لتقديم Shared Tasks: 25 أبريل 2026
✅ إشعارات القبول: 2 مايو 2026
🔗 رابط التقديم:
https://t.co/iaZlQGSzHy

Great news for the Arabic NLP community! 🎉
📢 Call for Shared Task Proposals is now open!

The first call for Shared Task Proposals for ArabicNLP 2026 is now out. The Fourth Arabic Natural Language Processing Conference will be co-located with EMNLP 2026 in Budapest, Hungary, on October 24–29, 2026.

🗓️ Shared Task proposal submission deadline: April 25, 2026
✅ Notification of acceptance: May 2, 2026

🔗 Submission link:
https://t.co/iaZlQGSzHy

#ArabicNLP2026 #ArabicNLP #SharedTasks #ArabicNLPSharedTasks #EMNLP2026

[Muhammad AbdulMageed @mageed]

I am very much looking forward to meeting colleagues at UPM and giving this talk. I will be covering our most recent research across various areas, including LLM work on Arabic and African languages, machine translation, model efficiency, and protein sequencing.

[جامعة الأمير مقرن بن عبدالعزيز @UPMMadinah]

#جامعة_الأمير_مقرن

[Demis Hassabis @demishassabis]

AlphaGenome is our latest & most advanced genomics model published in @Nature today including making the model & weights available to academic researchers. Can’t wait to see what the research community will do with it. Congrats to the team on our newest front cover! #AI4Science

[Muhammad AbdulMageed @mageed]

@ubcscience @UBCiSchool @UBCLinguistics @UBCLangScis

[Muhammad AbdulMageed @mageed]

I have openings for two Postdoctoral Fellows at UBC, with a starting date as soon as March 2026. A strong background and top-tier publications in speech and language processing and machine learning is required. Kindly share with strong candidates in your networks.

[Richard Sutton @RichardSSutton]

Very nice video covering the ideas and history of reinforcement learning. Love the original footage from the 1960s, 70s, and 80s.

[David Ifeoluwa Adelani 🇳🇬 @davlanade]

We are happy to announce a new shared task for @iwslt 2026 for African-Celtic speech translation

If you or your team are interested in furthering speech translation research for low-resource languages, then please check out https://t.co/rD6vxK6rOL .

https://t.co/7IH54Vmnu8

[Muhammad AbdulMageed @mageed]

Congratulations to all new ACL fellows!

[Muhammad AbdulMageed @mageed]

@UBC_CS @ubcscience @UBCiSchool @UBCLinguistics @UBCLangScis

[Muhammad AbdulMageed @mageed]

🙌 New Postdoctoral Opportunity

I am seeking an exceptional candidate for a Postdoctoral position focused on Next-Generation Language Modeling for High-Precision and Ultra-Fast Protein Sequencing. This position will be part of the UBC Health & AI Network. The fellow will work with me and my colleague Prof. Laks Lakshmanan.

[Muhammad AbdulMageed @mageed]

Job description and how to apply: https://t.co/1GLyJQw6um

[Muhammad AbdulMageed @mageed]

@DSFSI_Research Great work!