@inproceedings{li-specia-2019-comparison,
title = "A Comparison on Fine-grained Pre-trained Embeddings for the {WMT}19{C}hinese-{E}nglish News Translation Task",
author = "Li, Zhenhao and
Specia, Lucia",
booktitle = "Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)",
month = aug,
year = "2019",
address = "Florence, Italy",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/W19-5324",
doi = "10.18653/v1/W19-5324",
pages = "249--256",
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.",
}
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%0 Conference Proceedings
%T A Comparison on Fine-grained Pre-trained Embeddings for the WMT19Chinese-English News Translation Task
%A Li, Zhenhao
%A Specia, Lucia
%S Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1)
%D 2019
%8 aug
%I Association for Computational Linguistics
%C Florence, Italy
%F li-specia-2019-comparison
%X 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.
%R 10.18653/v1/W19-5324
%U https://aclanthology.org/W19-5324
%U https://doi.org/10.18653/v1/W19-5324
%P 249-256
Markdown (Informal)
[A Comparison on Fine-grained Pre-trained Embeddings for the WMT19Chinese-English News Translation Task](https://aclanthology.org/W19-5324) (Li & Specia, 2019)
ACL