Neural Speech Translation at AppTek
Evgeny Matusov, Patrick Wilken, Parnia Bahar, Julian Schamper, Pavel Golik, Albert Zeyer, Joan Albert Silvestre-Cerda, Adrià Martínez-Villaronga, Hendrik Pesch, Jan-Thorsten Peter
Abstract
This work describes AppTek’s speech translation pipeline that includes strong state-of-the-art automatic speech recognition (ASR) and neural machine translation (NMT) components. We show how these components can be tightly coupled by encoding ASR confusion networks, as well as ASR-like noise adaptation, vocabulary normalization, and implicit punctuation prediction during translation. In another experimental setup, we propose a direct speech translation approach that can be scaled to translation tasks with large amounts of text-only parallel training data but a limited number of hours of recorded and human-translated speech.- Anthology ID:
- 2018.iwslt-1.15
- Volume:
- Proceedings of the 15th International Conference on Spoken Language Translation
- Month:
- October 29-30
- Year:
- 2018
- Address:
- Brussels
- Editors:
- Marco Turchi, Jan Niehues, Marcello Frederico
- Venue:
- IWSLT
- SIG:
- SIGSLT
- Publisher:
- International Conference on Spoken Language Translation
- Note:
- Pages:
- 104–111
- Language:
- URL:
- https://aclanthology.org/2018.iwslt-1.15
- DOI:
- Cite (ACL):
- Evgeny Matusov, Patrick Wilken, Parnia Bahar, Julian Schamper, Pavel Golik, Albert Zeyer, Joan Albert Silvestre-Cerda, Adrià Martínez-Villaronga, Hendrik Pesch, and Jan-Thorsten Peter. 2018. Neural Speech Translation at AppTek. In Proceedings of the 15th International Conference on Spoken Language Translation, pages 104–111, Brussels. International Conference on Spoken Language Translation.
- Cite (Informal):
- Neural Speech Translation at AppTek (Matusov et al., IWSLT 2018)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-3/2018.iwslt-1.15.pdf