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    <abstract>In this work, we describe our submissions to two FinEval-2026 subtasks - Greek financial NER and Japanese implicit commitment recognition where both tasks contain small datasets at 400 and 253 labeled instances. For Subtask 1, we select ensemble models by error diversity rather than individual rank, and combine them via label routing: each label is taken from the model that performs best empirically on that label. For Subtask 2 we introduce ordinal minimal-pair augmentation, synthetically rewriting only the stance-bearing sentence-final clause of a real investor-relations response for a specific label on the commitment scale and verified by two judges from a different model family. Additionally, we perform style-matched generation conditioned on the target register and decode under the organizers’ published label distribution via constrained assignment. Style matching contributes more than every modeling change combined (+0.17). Our leaderboard scores are 0.8879 entity F1 for subtask 1 and 0.8571 accuracy for subtask 2.</abstract>
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%0 Conference Proceedings
%T Jetsons at the FinEval 2026: Minimal-Pair Augmentation and Label-Routed Ensembling for Greek and Japanese Financial NLP
%A Dakle, Parag Pravin
%A Rallabandi, Sai Krishna
%A Raghavan, Preethi
%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 dakle-etal-2026-jetsons
%X In this work, we describe our submissions to two FinEval-2026 subtasks - Greek financial NER and Japanese implicit commitment recognition where both tasks contain small datasets at 400 and 253 labeled instances. For Subtask 1, we select ensemble models by error diversity rather than individual rank, and combine them via label routing: each label is taken from the model that performs best empirically on that label. For Subtask 2 we introduce ordinal minimal-pair augmentation, synthetically rewriting only the stance-bearing sentence-final clause of a real investor-relations response for a specific label on the commitment scale and verified by two judges from a different model family. Additionally, we perform style-matched generation conditioned on the target register and decode under the organizers’ published label distribution via constrained assignment. Style matching contributes more than every modeling change combined (+0.17). Our leaderboard scores are 0.8879 entity F1 for subtask 1 and 0.8571 accuracy for subtask 2.
%U https://aclanthology.org/2026.finnlp-1.72/
%P 800-810

Markdown (Informal)

[Jetsons at the FinEval 2026: Minimal-Pair Augmentation and Label-Routed Ensembling for Greek and Japanese Financial NLP](https://aclanthology.org/2026.finnlp-1.72/) (Dakle et al., FinNLP 2026)

• Jetsons at the FinEval 2026: Minimal-Pair Augmentation and Label-Routed Ensembling for Greek and Japanese Financial NLP (Dakle et al., FinNLP 2026)

ACL

• Parag Pravin Dakle, Sai Krishna Rallabandi, and Preethi Raghavan. 2026. Jetsons at the FinEval 2026: Minimal-Pair Augmentation and Label-Routed Ensembling for Greek and Japanese Financial NLP. In Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing, pages 800–810, Budapest, Hungary. Association for Computational Linguistics.