# Alkhder team fine-tunes NileChat-3B for Arabic dialect translation

Digest AI · Research · published 2026-10-19T04:00:00Z

Canonical: https://digestai.news/story/alkhder-team-fine-tunes-nilechat-3b-for-arabic-dialect-translation

## Summary

Team Alkhder presented a system for the AlexandriaX-2026 shared task, focusing on translating English dialogue into thirteen distinct Arabic dialects. The approach involves fine-tuning the NileChat-3B-Base model using QLoRA on the Alexandria parallel dialogue corpus. A single set of adapter weights was trained to handle all dialects, with translations conditioned on specific factors such as the target country, domain, speaker roles, gender direction, and preceding dialogue context.

During inference, the system translates conversations autoregressively, feeding its own previous outputs back as context without relying on gold target-side test information. The training process is efficient, taking about one hour, while translating the private test set takes under two hours. The system achieved a country-macro-averaged spBLEU score of 20.36 and a chrF++ score of 36.64 on both tracks of the competition.

## Key points

- Team Alkhder fine-tuned NileChat-3B-Base with QLoRA for thirteen Arabic dialects.
- The system uses one adapter set for all dialects, conditioned on context and roles.
- It achieved a spBLEU of 20.36 and chrF++ of 36.64 on the AlexandriaX-2026 test set.

## Why it matters

This research demonstrates efficient methods for low-resource dialectal translation, potentially improving communication tools for Arabic speakers across different regions.

## Sources

1. [alkhder at AlexandriaX-2026: Context-Aware English-to-Dialectal Arabic Dialogue Translation with QLoRA-Fine-Tuned NileChat-3B](https://arxiv.org/abs/2610.22525) (arXiv cs.CL, 2026-10-19, primary source)

## Cite

Digest AI, "Alkhder team fine-tunes NileChat-3B for Arabic dialect translation", 19 October 2026, https://digestai.news/story/alkhder-team-fine-tunes-nilechat-3b-for-arabic-dialect-translation

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