Abstract
This paper describes our submission to the WMT 2019 Chinese-English (zh-en) news translation shared task. Our systems are based on RNN architectures with pre-trained embeddings which utilize character and sub-character information. We compare models with these different granularity levels using different evaluating metics. We find that a finer granularity embeddings can help the model according to character level evaluation and that the pre-trained embeddings can also be beneficial for model performance marginally when the training data is limited.- Anthology ID:
- W19-5324
- 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:
- 249–256
- Language:
- URL:
- https://aclanthology.org/W19-5324
- DOI:
- 10.18653/v1/W19-5324
- Cite (ACL):
- Zhenhao Li and Lucia Specia. 2019. A Comparison on Fine-grained Pre-trained Embeddings for the WMT19Chinese-English News Translation Task. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 249–256, Florence, Italy. Association for Computational Linguistics.
- Cite (Informal):
- A Comparison on Fine-grained Pre-trained Embeddings for the WMT19Chinese-English News Translation Task (Li & Specia, WMT 2019)
- PDF:
- https://preview.aclanthology.org/auto-file-uploads/W19-5324.pdf