End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning
Hou Jeung Han, Mohd Abbas Zaidi, Sathish Reddy Indurthi, Nikhil Kumar Lakumarapu, Beomseok Lee, Sangha Kim
Abstract
In this paper, we describe end-to-end simultaneous speech-to-text and text-to-text translation systems submitted to IWSLT2020 online translation challenge. The systems are built by adding wait-k and meta-learning approaches to the Transformer architecture. The systems are evaluated on different latency regimes. The simultaneous text-to-text translation achieved a BLEU score of 26.38 compared to the competition baseline score of 14.17 on the low latency regime (Average latency ≤ 3). The simultaneous speech-to-text system improves the BLEU score by 7.7 points over the competition baseline for the low latency regime (Average Latency ≤ 1000).- Anthology ID:
- 2020.iwslt-1.5
- Volume:
- Proceedings of the 17th International Conference on Spoken Language Translation
- Month:
- July
- Year:
- 2020
- Address:
- Online
- Editors:
- Marcello Federico, Alex Waibel, Kevin Knight, Satoshi Nakamura, Hermann Ney, Jan Niehues, Sebastian Stüker, Dekai Wu, Joseph Mariani, Francois Yvon
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 62–68
- Language:
- URL:
- https://aclanthology.org/2020.iwslt-1.5
- DOI:
- 10.18653/v1/2020.iwslt-1.5
- Cite (ACL):
- Hou Jeung Han, Mohd Abbas Zaidi, Sathish Reddy Indurthi, Nikhil Kumar Lakumarapu, Beomseok Lee, and Sangha Kim. 2020. End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning. In Proceedings of the 17th International Conference on Spoken Language Translation, pages 62–68, Online. Association for Computational Linguistics.
- Cite (Informal):
- End-to-End Simultaneous Translation System for IWSLT2020 Using Modality Agnostic Meta-Learning (Han et al., IWSLT 2020)
- PDF:
- https://preview.aclanthology.org/corrections-2024-07/2020.iwslt-1.5.pdf