@inproceedings{jang-etal-2026-explicit,
title = "Explicit State Gating and Implicit Refusal for Unreadable Scene Text",
author = "Jang, Dayeon and
Bae, Yuseok and
Yun, Kimin",
editor = {Aziz, Wilker and
Berant, Jonathan and
Eikema, Bryan and
Ilia, Evgenia and
de Marneffe, Marie-Catherine and
Plank, Barbara and
Shelmanov, Artem and
Swayamdipta, Swabha and
Tiedemann, J{\"o}rg and
Vazhentsev, Artem and
V{\'a}zquez, Ra{\'u}l and
Zerva, Chrysoula},
booktitle = "The Proceedings of the Third Workshop on Uncertainty-Aware {NLP}",
month = oct,
year = "2026",
address = "Budapest, Hungary",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2026.uncertainlp-main.16/",
pages = "275--279",
ISBN = "979-8-89176-486-6",
abstract = "Scene text recognition systems must decide what to do when an image contains no reliably readable text. We compare two abstention contracts for a vision-language scene-text recognizer. Under implicit refusal, the model signals unreadability inside its own transcription output. Under explicit state gating, a separate five-way state classifier decides whether a transcription is emitted at all. Across four training variants sharing the same backbone, direct OCR yields similar accuracy on readable text; the two contracts differ mainly in how they handle inputs with no readable text. Gating reduces false transcription (emitting text where none is readable) from 45.9{\%} to 1.7{\%}, at the cost of falsely abstaining on 3.8{\%} of readable inputs. Which contract is preferable, however, cannot be determined without first specifying how much a correct abstention is worth. When rejected inputs are excluded from scoring, all gated configurations show similar risk; when a correct abstention is credited as a useful outcome, gating lowers risk on the diagnostic set. We present this preliminary analysis not as a context-free model ranking, but as a framework for choosing an abstention contract at deployment time. These results motivate future systems that use the predicted state to select a recovery action before abstaining."
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%0 Conference Proceedings
%T Explicit State Gating and Implicit Refusal for Unreadable Scene Text
%A Jang, Dayeon
%A Bae, Yuseok
%A Yun, Kimin
%Y Aziz, Wilker
%Y Berant, Jonathan
%Y Eikema, Bryan
%Y Ilia, Evgenia
%Y de Marneffe, Marie-Catherine
%Y Plank, Barbara
%Y Shelmanov, Artem
%Y Swayamdipta, Swabha
%Y Tiedemann, Jörg
%Y Vazhentsev, Artem
%Y Vázquez, Raúl
%Y Zerva, Chrysoula
%S The Proceedings of the Third Workshop on Uncertainty-Aware NLP
%D 2026
%8 October
%I Association for Computational Linguistics
%C Budapest, Hungary
%@ 979-8-89176-486-6
%F jang-etal-2026-explicit
%X Scene text recognition systems must decide what to do when an image contains no reliably readable text. We compare two abstention contracts for a vision-language scene-text recognizer. Under implicit refusal, the model signals unreadability inside its own transcription output. Under explicit state gating, a separate five-way state classifier decides whether a transcription is emitted at all. Across four training variants sharing the same backbone, direct OCR yields similar accuracy on readable text; the two contracts differ mainly in how they handle inputs with no readable text. Gating reduces false transcription (emitting text where none is readable) from 45.9% to 1.7%, at the cost of falsely abstaining on 3.8% of readable inputs. Which contract is preferable, however, cannot be determined without first specifying how much a correct abstention is worth. When rejected inputs are excluded from scoring, all gated configurations show similar risk; when a correct abstention is credited as a useful outcome, gating lowers risk on the diagnostic set. We present this preliminary analysis not as a context-free model ranking, but as a framework for choosing an abstention contract at deployment time. These results motivate future systems that use the predicted state to select a recovery action before abstaining.
%U https://aclanthology.org/2026.uncertainlp-main.16/
%P 275-279
Markdown (Informal)
[Explicit State Gating and Implicit Refusal for Unreadable Scene Text](https://aclanthology.org/2026.uncertainlp-main.16/) (Jang et al., UncertaiNLP 2026)
ACL