Rosetta at AlexandriaX-2026: LoRA-Adapted NileChat for Context-Aware Dialectal Arabic Dialogue Translation
Not on the map. Why?
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.
Key facts. Cited by 1 paper 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
11 of 24 references.
References · 11
- 2016Google's Neural Machine Translation System: Bridging the Gap between Human and Machine Translationcited by 1,475
- 2022No Language Left Behind: Scaling Human-Centered Machine Translationcited by 1,312
- 2023A Multitask, Multilingual, Multimodal Evaluation of ChatGPT on Reasoning, Hallucination, and Interactivitycited by 894
- 2015Sequence Level Training with Recurrent Neural Networkscited by 576
- 2021The FLORES-101 Evaluation Benchmark for Low-Resource and Multilingual Machine Translationcited by 560
- 20218-bit Optimizers via Block-wise Quantizationcited by 403
- 2023Having Beer after Prayer? Measuring Cultural Bias in Large Language Modelscited by 153
- 2023TARJAMAT: Evaluation of Bard and ChatGPT on Machine Translation of Ten Arabic Varietiescited by 16
- 2025NileChat: Towards Linguistically Diverse and Culturally Aware LLMs for Local Communitiescited by 13
- 2024AraFinNLP 2024: The First Arabic Financial NLP Shared Taskcited by 12
- 2025Languages Still Left Behind: Toward a Better Multilingual Machine Translation Benchmarkcited by 7
Built on by
Citing papers per year: 2026: 1.
1 paper.