Abstract
We present our submission to the WMT19 Robustness Task. Our baseline system is the Charles University (CUNI) Transformer system trained for the WMT18 shared task on News Translation. Quantitative results show that the CUNI Transformer system is already far more robust to noisy input than the LSTM-based baseline provided by the task organizers. We further improved the performance of our model by fine-tuning on the in-domain noisy data without influencing the translation quality on the news domain.- Anthology ID:
- W19-5364
- Volume:
- Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
- Month:
- August
- Year:
- 2019
- Address:
- Florence, Italy
- Venue:
- WMT
- SIG:
- SIGMT
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 539–543
- Language:
- URL:
- https://aclanthology.org/W19-5364
- DOI:
- 10.18653/v1/W19-5364
- Cite (ACL):
- Jindřich Helcl, Jindřich Libovický, and Martin Popel. 2019. CUNI System for the WMT19 Robustness Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 539–543, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- CUNI System for the WMT19 Robustness Task (Helcl et al., WMT 2019)
- PDF:
- https://preview.aclanthology.org/ingestion-script-update/W19-5364.pdf
- Data
- MTNT, WMT 2014