@inproceedings{kapoor-etal-2026-maddies,
title = "Maddies {NLP} at the {F}in{E}val 2026: Proposition-Aware Tagged Reasoning for {J}apanese Implicit Communication Recognition",
author = "Kapoor, Madhav and
Channa, Aditya and
Singh, Jastej",
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-1.64/",
pages = "745--749",
ISBN = "979-8-89176-466-8",
abstract = "We describe our system for the FinNLP 2026 Japanese Financial Implicit Communication Recognition (JF-ICR) shared task. The task requires assigning one of five ordinal commitment and refusal labels to a Japanese investor-relations question and company response. Our final system uses the 30.7B-parameter OpenRouter model `google/gemma-4-31b-it{`} in a two-stage, proposition-aware workflow. A label-blind first stage extracts tagged evidence for the target proposition, modality, actor and controllability, negation scope, conditionality, and institutional anchors. A second stage re-reads the original Japanese with this evidence and emits the final label before an optional rationale. On 253 labeled development examples, the system reaches 60.08{\%} accuracy versus 56.92{\%} for our earlier single-call control, with no invalid labels. The frozen submission scores 93.33{\%} (14/15) publicly and 94.29{\%} (33/35) privately. Deeper decision trees, latent-score mapping, and parallel judging all reduce accuracy, favoring compact decomposition with strict output control."
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<abstract>We describe our system for the FinNLP 2026 Japanese Financial Implicit Communication Recognition (JF-ICR) shared task. The task requires assigning one of five ordinal commitment and refusal labels to a Japanese investor-relations question and company response. Our final system uses the 30.7B-parameter OpenRouter model ‘google/gemma-4-31b-it‘ in a two-stage, proposition-aware workflow. A label-blind first stage extracts tagged evidence for the target proposition, modality, actor and controllability, negation scope, conditionality, and institutional anchors. A second stage re-reads the original Japanese with this evidence and emits the final label before an optional rationale. On 253 labeled development examples, the system reaches 60.08% accuracy versus 56.92% for our earlier single-call control, with no invalid labels. The frozen submission scores 93.33% (14/15) publicly and 94.29% (33/35) privately. Deeper decision trees, latent-score mapping, and parallel judging all reduce accuracy, favoring compact decomposition with strict output control.</abstract>
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%0 Conference Proceedings
%T Maddies NLP at the FinEval 2026: Proposition-Aware Tagged Reasoning for Japanese Implicit Communication Recognition
%A Kapoor, Madhav
%A Channa, Aditya
%A Singh, Jastej
%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 kapoor-etal-2026-maddies
%X We describe our system for the FinNLP 2026 Japanese Financial Implicit Communication Recognition (JF-ICR) shared task. The task requires assigning one of five ordinal commitment and refusal labels to a Japanese investor-relations question and company response. Our final system uses the 30.7B-parameter OpenRouter model ‘google/gemma-4-31b-it‘ in a two-stage, proposition-aware workflow. A label-blind first stage extracts tagged evidence for the target proposition, modality, actor and controllability, negation scope, conditionality, and institutional anchors. A second stage re-reads the original Japanese with this evidence and emits the final label before an optional rationale. On 253 labeled development examples, the system reaches 60.08% accuracy versus 56.92% for our earlier single-call control, with no invalid labels. The frozen submission scores 93.33% (14/15) publicly and 94.29% (33/35) privately. Deeper decision trees, latent-score mapping, and parallel judging all reduce accuracy, favoring compact decomposition with strict output control.
%U https://aclanthology.org/2026.finnlp-1.64/
%P 745-749
Markdown (Informal)
[Maddies NLP at the FinEval 2026: Proposition-Aware Tagged Reasoning for Japanese Implicit Communication Recognition](https://aclanthology.org/2026.finnlp-1.64/) (Kapoor et al., FinNLP 2026)
ACL