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alkhder at AlexandriaX-2026: Context-Aware English-to-Dialectal Arabic Dialogue Translation with QLoRA-Fine-Tuned NileChat-3B

Team Alkhder's system for Subtask 1 of the AlexandriaX-2026 shared task, which translates English dialogue turns into thirteen Arabic dialects, reached a country-macro-averaged spBLEU of 20.36 and chrF++ of 36.64 on both tracks, according to the team's arXiv paper. The system fine-tunes NileChat-3B-Base with QLoRA on the Alexandria parallel dialogue corpus using one set of adapter weights for all dialects, conditioning each turn on target country and dialect, domain, participant roles, speaker-addressee gender direction, and preceding dialogue context. Under the constrained track using only Alexandria data, it trains in about one hour and translates the private test set in under two hours, feeding the model its own previous outputs back as context without gold target-side test information.

by read1 min views1 publishedOct 19, 2026

arXiv:2610.22525v1 Announce Type: new Abstract: This paper describes team Alkhder's submission to Subtask 1 of the AlexandriaX-2026 shared task, which asks systems to translate English dialogue turns into thirteen Arabic dialects. We fine-tune NileChat-3B-Base with QLoRA on the Alexandria parallel dialogue corpus, training one set of adapter weights for all dialects. Each turn is translated conditioned on the target country and dialect, the domain, the participant roles, the speaker-addressee gender direction, and the preceding dialogue context. At inference, conversations are translated autoregressively and the model is fed its own previous outputs back as context without using any gold target-side test information. Under the constrained track, using only Alexandria data, it trains in about one hour and translates the private test set in under two hours. The system reaches a country-macro-averaged spBLEU of 20.36 and chrF++ of 36.64 on both tracks.

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