@inproceedings{wang-etal-2020-curriculum,
title = "Curriculum Pre-training for End-to-End Speech Translation",
author = "Wang, Chengyi and
Wu, Yu and
Liu, Shujie and
Zhou, Ming and
Yang, Zhenglu",
editor = "Jurafsky, Dan and
Chai, Joyce and
Schluter, Natalie and
Tetreault, Joel",
booktitle = "Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.acl-main.344",
doi = "10.18653/v1/2020.acl-main.344",
pages = "3728--3738",
abstract = "End-to-end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features. However, we argue that pre-training the encoder only through simple speech recognition is not enough, and high-level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.",
}
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<abstract>End-to-end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features. However, we argue that pre-training the encoder only through simple speech recognition is not enough, and high-level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.</abstract>
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%0 Conference Proceedings
%T Curriculum Pre-training for End-to-End Speech Translation
%A Wang, Chengyi
%A Wu, Yu
%A Liu, Shujie
%A Zhou, Ming
%A Yang, Zhenglu
%Y Jurafsky, Dan
%Y Chai, Joyce
%Y Schluter, Natalie
%Y Tetreault, Joel
%S Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F wang-etal-2020-curriculum
%X End-to-end speech translation poses a heavy burden on the encoder because it has to transcribe, understand, and learn cross-lingual semantics simultaneously. To obtain a powerful encoder, traditional methods pre-train it on ASR data to capture speech features. However, we argue that pre-training the encoder only through simple speech recognition is not enough, and high-level linguistic knowledge should be considered. Inspired by this, we propose a curriculum pre-training method that includes an elementary course for transcription learning and two advanced courses for understanding the utterance and mapping words in two languages. The difficulty of these courses is gradually increasing. Experiments show that our curriculum pre-training method leads to significant improvements on En-De and En-Fr speech translation benchmarks.
%R 10.18653/v1/2020.acl-main.344
%U https://aclanthology.org/2020.acl-main.344
%U https://doi.org/10.18653/v1/2020.acl-main.344
%P 3728-3738
Markdown (Informal)
[Curriculum Pre-training for End-to-End Speech Translation](https://aclanthology.org/2020.acl-main.344) (Wang et al., ACL 2020)
ACL
- Chengyi Wang, Yu Wu, Shujie Liu, Ming Zhou, and Zhenglu Yang. 2020. Curriculum Pre-training for End-to-End Speech Translation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 3728–3738, Online. Association for Computational Linguistics.