Abstract
We explore different model architectures for the WMT 19 shared task on word-level quality estimation of automatic translation. We start with a model similar to Shef-bRNN, which we modify by using conditional random fields for sequence labelling. Additionally, we use a different approach for labelling gaps and source words. We further develop this model by including features from different sources such as BERT, baseline features for the task and transformer encoders. We evaluate the performance of our models on the English-German dataset for the corresponding shared task.- Anthology ID:
- W19-5408
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Editors:
- Ondřej Bojar, Rajen Chatterjee, Christian Federmann, Mark Fishel, Yvette Graham, Barry Haddow, Matthias Huck, Antonio Jimeno Yepes, Philipp Koehn, André Martins, Christof Monz, Matteo Negri, Aurélie Névéol, Mariana Neves, Matt Post, Marco Turchi, Karin Verspoor
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 90–94
- Language:
- URL:
- https://aclanthology.org/W19-5408
- DOI:
- 10.18653/v1/W19-5408
- Cite (ACL):
- Mikhail Mosyagin and Varvara Logacheva. 2019. MIPT System for World-Level Quality Estimation. In Proceedings of the Fourth Conference on Machine Translation (Volume 3: Shared Task Papers, Day 2), pages 90–94, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- MIPT System for World-Level Quality Estimation (Mosyagin & Logacheva, WMT 2019)
- PDF:
- https://preview.aclanthology.org/nschneid-patch-5/W19-5408.pdf