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    <abstract>We describe our submission to FinEval 2026 Subtask 3 (Multilingual Financial Question Answering). We compare four retrieval strategies and find dense embedding-based retrieval strongest, consistent with its ability to match semantically equivalent content across languages with no lexical overlap. We then ablate prompt-level and inference-time techniques on top of dense retrieval, validating each addition with cross-validation; a category-conditioned router that appeared promising in-sample failed to generalize and was discarded. Our final development-set configuration length-constrained, few-shot, and corrected for a measured English-only citation bias achieves 0.412 ROUGE-1 under 5-fold cross-validation, and our separately tuned test-set submission achieves 0.3275 F1 on the public leaderboard (0.3593 private).</abstract>
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%0 Conference Proceedings
%T Finaira at the FinEval 2026: Dense Retrieval and Validated Prompt Engineering for Cross-Lingual Financial Question Answering
%A Nasser, Yara Mohamed
%A Mohamed, Ashraf
%Y Huang, Jimin
%Y Lee, Yongjae
%Y Ananiadou, Sophia
%Y Chen, Hsin-Hsi
%Y Takamura, Hiroya
%Y Chen, Chung-Chi
%S Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-466-8
%F nasser-mohamed-2026-finaira
%X We describe our submission to FinEval 2026 Subtask 3 (Multilingual Financial Question Answering). We compare four retrieval strategies and find dense embedding-based retrieval strongest, consistent with its ability to match semantically equivalent content across languages with no lexical overlap. We then ablate prompt-level and inference-time techniques on top of dense retrieval, validating each addition with cross-validation; a category-conditioned router that appeared promising in-sample failed to generalize and was discarded. Our final development-set configuration length-constrained, few-shot, and corrected for a measured English-only citation bias achieves 0.412 ROUGE-1 under 5-fold cross-validation, and our separately tuned test-set submission achieves 0.3275 F1 on the public leaderboard (0.3593 private).
%U https://aclanthology.org/2026.finnlp-1.67/
%P 762-769

Markdown (Informal)

[Finaira at the FinEval 2026: Dense Retrieval and Validated Prompt Engineering for Cross-Lingual Financial Question Answering](https://aclanthology.org/2026.finnlp-1.67/) (Nasser & Mohamed, FinNLP 2026)

• Finaira at the FinEval 2026: Dense Retrieval and Validated Prompt Engineering for Cross-Lingual Financial Question Answering (Nasser & Mohamed, FinNLP 2026)

ACL

• Yara Mohamed Nasser and Ashraf Mohamed. 2026. Finaira at the FinEval 2026: Dense Retrieval and Validated Prompt Engineering for Cross-Lingual Financial Question Answering. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 762–769, Budapest, Hungary. Association for Computational Linguistics.