@inproceedings{jiang-etal-2024-contextual-modeling,
title = "Contextual Modeling for Document-level {ASR} Error Correction",
author = "Jiang, Jin and
Yin, Xunjian and
Wan, Xiaojun and
Peng, Wei and
Li, Rongjun and
Yang, Jingyuan and
Zhou, Yanquan",
editor = "Calzolari, Nicoletta and
Kan, Min-Yen and
Hoste, Veronique and
Lenci, Alessandro and
Sakti, Sakriani and
Xue, Nianwen",
booktitle = "Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)",
month = may,
year = "2024",
address = "Torino, Italia",
publisher = "ELRA and ICCL",
url = "https://aclanthology.org/2024.lrec-main.341",
pages = "3855--3867",
abstract = "Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a $k$-Nearest Neighbors ($k$NN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.",
}
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<abstract>Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a k-Nearest Neighbors (kNN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.</abstract>
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%0 Conference Proceedings
%T Contextual Modeling for Document-level ASR Error Correction
%A Jiang, Jin
%A Yin, Xunjian
%A Wan, Xiaojun
%A Peng, Wei
%A Li, Rongjun
%A Yang, Jingyuan
%A Zhou, Yanquan
%Y Calzolari, Nicoletta
%Y Kan, Min-Yen
%Y Hoste, Veronique
%Y Lenci, Alessandro
%Y Sakti, Sakriani
%Y Xue, Nianwen
%S Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024)
%D 2024
%8 May
%I ELRA and ICCL
%C Torino, Italia
%F jiang-etal-2024-contextual-modeling
%X Contextual information, including the sentences in the same document and in other documents of the dataset, plays a crucial role in improving the accuracy of document-level ASR Error Correction (AEC), while most previous works ignore this. In this paper, we propose a context-aware method that utilizes a k-Nearest Neighbors (kNN) approach to enhance the AEC model by retrieving a datastore containing contextual information. We conduct experiments on two English and two Chinese datasets, and the results demonstrate that our proposed model can effectively utilize contextual information to improve document-level AEC. Furthermore, the context information from the whole dataset provides even better results.
%U https://aclanthology.org/2024.lrec-main.341
%P 3855-3867
Markdown (Informal)
[Contextual Modeling for Document-level ASR Error Correction](https://aclanthology.org/2024.lrec-main.341) (Jiang et al., LREC-COLING 2024)
ACL
- Jin Jiang, Xunjian Yin, Xiaojun Wan, Wei Peng, Rongjun Li, Jingyuan Yang, and Yanquan Zhou. 2024. Contextual Modeling for Document-level ASR Error Correction. In Proceedings of the 2024 Joint International Conference on Computational Linguistics, Language Resources and Evaluation (LREC-COLING 2024), pages 3855–3867, Torino, Italia. ELRA and ICCL.