# Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation

arXiv: https://arxiv.org/abs/2609.10395 · HTML: https://mlsanity.com/paper/2609.10395/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 11 of its 24 references that are in the map.
- First posted on arXiv 9 Sep 2026.

## Builds on (most cited first, 11 of 11)

- [Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translation](https://mlsanity.com/paper/1609.08144/) (2016)
- [No Language Left Behind: Scaling Human-Centered Machine Translation](https://mlsanity.com/paper/2207.04672/) (2022)
- [A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivity](https://mlsanity.com/paper/2302.04023/) (2023)
- [Sequence Level Training with Recurrent Neural Networks](https://mlsanity.com/paper/1511.06732/) (2015)
- [The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translation](https://mlsanity.com/paper/2106.03193/) (2021)
- [8-bit Optimizers via Block-wise Quantization](https://mlsanity.com/paper/2110.02861/) (2021)
- [Having Beer after Prayer? Measuring Cultural Bias in Large Language Models](https://mlsanity.com/paper/2305.14456/) (2023)
- [TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varieties](https://mlsanity.com/paper/2308.03051/) (2023)
- [NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communities](https://mlsanity.com/paper/2505.18383/) (2025)
- [AraFinNLP 2024: The First Arabic Financial NLP Shared Task](https://mlsanity.com/paper/2407.09818/) (2024)
- [Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmark](https://mlsanity.com/paper/2508.20511/) (2025)

## Built on by (most cited first, 1 of 1)

- [AlexandriaX 2026: The First Shared Task on Dialectal Arabic Machine Translation](https://mlsanity.com/paper/2609.22796/) (2026)

## Abstract (arXiv)

This paper describes the Rosetta system for Subtask 1 (Context-Aware English-to-Dialectal Arabic Dialogue Translation) of the AlexandriaX shared task, participating in both constrained and unconstrained tracks. The approach fine-tunes a LoRA adapter on NileChat-3B using structured system/user prompts that condition generation on dialect and dialogue context. For the unconstrained track, the adapter is additionally pretrained on MADAR and PADIC. Rosetta ranked 4th in the constrained track (spBLEU 26.10) and 5th in the unconstrained track (spBLEU 25.09). The experimental results demonstrate that external pretraining helps only two of thirteen dialects while slightly hurting overall performance, suggesting negative transfer.