@inproceedings{bei-etal-2019-gtcom,
title = "{GTCOM} Neural Machine Translation Systems for {WMT}19",
author = "Bei, Chao and
Zong, Hao and
Yuan, Conghu and
Liu, Qingming and
Fan, Baoyong",
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/W19-5305",
doi = "10.18653/v1/W19-5305",
pages = "116--121",
abstract = "This paper describes the Global Tone Communication Co., Ltd.{'}s submission of the WMT19 shared news translation task. We participate in six directions: English to (Gujarati, Lithuanian and Finnish) and (Gujarati, Lithuanian and Finnish) to English. Further, we get the best BLEU scores in the directions of English to Gujarati and Lithuanian to English (28.2 and 36.3 respectively) among all the participants. The submitted systems mainly focus on back-translation, knowledge distillation and reranking to build a competitive model for this task. Also, we apply language model to filter monolingual data, back-translated data and parallel data. The techniques we apply for data filtering include filtering by rules, language models. Besides, We conduct several experiments to validate different knowledge distillation techniques and right-to-left (R2L) reranking.",
}
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%0 Conference Proceedings
%T GTCOM Neural Machine Translation Systems for WMT19
%A Bei, Chao
%A Zong, Hao
%A Yuan, Conghu
%A Liu, Qingming
%A Fan, Baoyong
%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 bei-etal-2019-gtcom
%X This paper describes the Global Tone Communication Co., Ltd.’s submission of the WMT19 shared news translation task. We participate in six directions: English to (Gujarati, Lithuanian and Finnish) and (Gujarati, Lithuanian and Finnish) to English. Further, we get the best BLEU scores in the directions of English to Gujarati and Lithuanian to English (28.2 and 36.3 respectively) among all the participants. The submitted systems mainly focus on back-translation, knowledge distillation and reranking to build a competitive model for this task. Also, we apply language model to filter monolingual data, back-translated data and parallel data. The techniques we apply for data filtering include filtering by rules, language models. Besides, We conduct several experiments to validate different knowledge distillation techniques and right-to-left (R2L) reranking.
%R 10.18653/v1/W19-5305
%U https://aclanthology.org/W19-5305
%U https://doi.org/10.18653/v1/W19-5305
%P 116-121
Markdown (Informal)
[GTCOM Neural Machine Translation Systems for WMT19](https://aclanthology.org/W19-5305) (Bei et al., 2019)
ACL
- Chao Bei, Hao Zong, Conghu Yuan, Qingming Liu, and Baoyong Fan. 2019. GTCOM Neural Machine Translation Systems for WMT19. In Proceedings of the Fourth Conference on Machine Translation (Volume 2: Shared Task Papers, Day 1), pages 116–121, Florence, Italy. Association for Computational Linguistics.