@inproceedings{rashid-etal-2026-fin,
title = "{F}in{\_}{A}gent at {F}in{E}val 2026: Task-Aware Methods for Multilingual Financial {NLP}",
author = "Rashid, Mohotarema and
Hossain, Tanzina and
Song, Xiaoying and
Hong, Lingzi",
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-1.73/",
pages = "811--821",
ISBN = "979-8-89176-466-8",
abstract = "We present Fin{\_}Agent, our submission to the FinEval 2026 shared task on multilingual financial language understanding. Our systems address Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. For Greek NER, we combine annotation-aware numeric rules with zero-shot Qwen2.5-32B classification, achieving 0.9521 public F1 and 0.8884 private F1. For Japanese implicit communication, an interpretable cue-based ordinal scorer achieves 98.0{\%} test accuracy. For multilingual financial QA, BGE-M3 retrieval with Qwen3-32B generation achieves 0.3023 and 0.3419 ROUGE-1 F1 on the public and private splits. Key limitations include dataset-specific rules, handcrafted cues, and limited supervision. Implementation code is available https://github.com/Rashidnira/finagent-fineval-2026."
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%0 Conference Proceedings
%T Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP
%A Rashid, Mohotarema
%A Hossain, Tanzina
%A Song, Xiaoying
%A Hong, Lingzi
%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 rashid-etal-2026-fin
%X We present Fin_Agent, our submission to the FinEval 2026 shared task on multilingual financial language understanding. Our systems address Greek financial named-entity recognition, Japanese implicit communication recognition, and multilingual financial question answering. For Greek NER, we combine annotation-aware numeric rules with zero-shot Qwen2.5-32B classification, achieving 0.9521 public F1 and 0.8884 private F1. For Japanese implicit communication, an interpretable cue-based ordinal scorer achieves 98.0% test accuracy. For multilingual financial QA, BGE-M3 retrieval with Qwen3-32B generation achieves 0.3023 and 0.3419 ROUGE-1 F1 on the public and private splits. Key limitations include dataset-specific rules, handcrafted cues, and limited supervision. Implementation code is available https://github.com/Rashidnira/finagent-fineval-2026.
%U https://aclanthology.org/2026.finnlp-1.73/
%P 811-821
Markdown (Informal)
[Fin_Agent at FinEval 2026: Task-Aware Methods for Multilingual Financial NLP](https://aclanthology.org/2026.finnlp-1.73/) (Rashid et al., FinNLP 2026)
ACL