@inproceedings{lin-etal-2025-neko,
title = "{N}e{K}o: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model",
author = "Lin, Yen-Ting and
Chen, Zhehuai and
Zelasko, Piotr and
Wan, Zhen and
Yang, Xuesong and
Chen, Zih-Ching and
Puvvada, Krishna C and
Hu, Ke and
Fu, Szu-Wei and
Chiu, Jun Wei and
Balam, Jagadeesh and
Ginsburg, Boris and
Wang, Yu-Chiang Frank and
Yang, Chao-Han Huck",
editor = "Rehm, Georg and
Li, Yunyao",
booktitle = "Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)",
month = jul,
year = "2025",
address = "Vienna, Austria",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2025.acl-industry.17/",
doi = "10.18653/v1/2025.acl-industry.17",
pages = "222--236",
ISBN = "979-8-89176-288-6",
abstract = "Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an ``expert'' of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset{'}s tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative 5.0{\%} WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-3.5-Sonnet with 15.5{\%} to 27.6{\%} relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model."
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<abstract>Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an “expert” of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset’s tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative 5.0% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-3.5-Sonnet with 15.5% to 27.6% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model.</abstract>
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%0 Conference Proceedings
%T NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model
%A Lin, Yen-Ting
%A Chen, Zhehuai
%A Zelasko, Piotr
%A Wan, Zhen
%A Yang, Xuesong
%A Chen, Zih-Ching
%A Puvvada, Krishna C.
%A Hu, Ke
%A Fu, Szu-Wei
%A Chiu, Jun Wei
%A Balam, Jagadeesh
%A Ginsburg, Boris
%A Wang, Yu-Chiang Frank
%A Yang, Chao-Han Huck
%Y Rehm, Georg
%Y Li, Yunyao
%S Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track)
%D 2025
%8 July
%I Association for Computational Linguistics
%C Vienna, Austria
%@ 979-8-89176-288-6
%F lin-etal-2025-neko
%X Construction of a general-purpose post-recognition error corrector poses a crucial question: how can we most effectively train a model on a large mixture of domain datasets? The answer would lie in learning dataset-specific features and digesting their knowledge in a single model. Previous methods achieve this by having separate correction language models, resulting in a significant increase in parameters. In this work, we present Mixture-of-Experts as a solution, highlighting that MoEs are much more than a scalability tool. We propose a Multi-Task Correction MoE, where we train the experts to become an “expert” of speech-to-text, language-to-text and vision-to-text datasets by learning to route each dataset’s tokens to its mapped expert. Experiments on the Open ASR Leaderboard show that we explore a new state-of-the-art performance by achieving an average relative 5.0% WER reduction and substantial improvements in BLEU scores for speech and translation tasks. On zero-shot evaluation, NeKo outperforms GPT-3.5 and Claude-3.5-Sonnet with 15.5% to 27.6% relative WER reduction in the Hyporadise benchmark. NeKo performs competitively on grammar and post-OCR correction as a multi-task model.
%R 10.18653/v1/2025.acl-industry.17
%U https://aclanthology.org/2025.acl-industry.17/
%U https://doi.org/10.18653/v1/2025.acl-industry.17
%P 222-236
Markdown (Informal)
[NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model](https://aclanthology.org/2025.acl-industry.17/) (Lin et al., ACL 2025)
ACL
- Yen-Ting Lin, Zhehuai Chen, Piotr Zelasko, Zhen Wan, Xuesong Yang, Zih-Ching Chen, Krishna C Puvvada, Ke Hu, Szu-Wei Fu, Jun Wei Chiu, Jagadeesh Balam, Boris Ginsburg, Yu-Chiang Frank Wang, and Chao-Han Huck Yang. 2025. NeKo: Cross-Modality Post-Recognition Error Correction with Tasks-Guided Mixture-of-Experts Language Model. In Proceedings of the 63rd Annual Meeting of the Association for Computational Linguistics (Volume 6: Industry Track), pages 222–236, Vienna, Austria. Association for Computational Linguistics.