@inproceedings{cao-etal-2026-fineval,
title = "{F}in{E}val Challenge @ {EMNLP} 2026 - {F}in{NLP} Workshop: Multilingual Financial Language Understanding",
author = "Cao, Yupeng and
Li, Haohang and
Jiang, Yuechen and
He, Yueru and
Wang, Yuyan and
Xiang, Ruoyu and
Yu, Yangyang",
editor = "Huang, Jimin and
Lee, Yongjae and
Ananiadou, Sophia and
Chen, Hsin-Hsi and
Takamura, Hiroya and
Chen, Chung-Chi",
booktitle = "Proceedings of the 11th Workshop on Financial Technology and Natural Language Processing",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.finnlp-1.62/",
pages = "730--739",
ISBN = "979-8-89176-466-8",
abstract = "Financial language understanding requires accurate interpretation of numerical disclosures, implicit corporate intent, and evidence distributed across languages and documents. We present FinEval 2026, a shared task at the FinNLP workshop at EMNLP 2026, comprising Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. The registration records contain 35 teams after reconciliation, and 14 teams contribute system description papers. The final leaderboards contain 7, 10, and 15 entries for the three subtasks, respectively. We describe the task design, participation, submitted approaches, and final results. Leading scores reach 0.9676 entity F1, 0.9714 accuracy, and 0.3827 ROUGE-1 F1. Systems combine domain rules, language-specific modeling, structured prompting, retrieval, and controlled answer revision. Their results highlight the value of task-specific constraints while exposing limitations arising from small test sets, development-to-test shifts, and reuse of previously released reference material. We identify priorities for more reliable multilingual financial evaluation."
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<abstract>Financial language understanding requires accurate interpretation of numerical disclosures, implicit corporate intent, and evidence distributed across languages and documents. We present FinEval 2026, a shared task at the FinNLP workshop at EMNLP 2026, comprising Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. The registration records contain 35 teams after reconciliation, and 14 teams contribute system description papers. The final leaderboards contain 7, 10, and 15 entries for the three subtasks, respectively. We describe the task design, participation, submitted approaches, and final results. Leading scores reach 0.9676 entity F1, 0.9714 accuracy, and 0.3827 ROUGE-1 F1. Systems combine domain rules, language-specific modeling, structured prompting, retrieval, and controlled answer revision. Their results highlight the value of task-specific constraints while exposing limitations arising from small test sets, development-to-test shifts, and reuse of previously released reference material. We identify priorities for more reliable multilingual financial evaluation.</abstract>
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%0 Conference Proceedings
%T FinEval Challenge @ EMNLP 2026 - FinNLP Workshop: Multilingual Financial Language Understanding
%A Cao, Yupeng
%A Li, Haohang
%A Jiang, Yuechen
%A He, Yueru
%A Wang, Yuyan
%A Xiang, Ruoyu
%A Yu, Yangyang
%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 cao-etal-2026-fineval
%X Financial language understanding requires accurate interpretation of numerical disclosures, implicit corporate intent, and evidence distributed across languages and documents. We present FinEval 2026, a shared task at the FinNLP workshop at EMNLP 2026, comprising Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. The registration records contain 35 teams after reconciliation, and 14 teams contribute system description papers. The final leaderboards contain 7, 10, and 15 entries for the three subtasks, respectively. We describe the task design, participation, submitted approaches, and final results. Leading scores reach 0.9676 entity F1, 0.9714 accuracy, and 0.3827 ROUGE-1 F1. Systems combine domain rules, language-specific modeling, structured prompting, retrieval, and controlled answer revision. Their results highlight the value of task-specific constraints while exposing limitations arising from small test sets, development-to-test shifts, and reuse of previously released reference material. We identify priorities for more reliable multilingual financial evaluation.
%U https://aclanthology.org/2026.finnlp-1.62/
%P 730-739
Markdown (Informal)
[FinEval Challenge @ EMNLP 2026 - FinNLP Workshop: Multilingual Financial Language Understanding](https://aclanthology.org/2026.finnlp-1.62/) (Cao et al., FinNLP 2026)
ACL