@inproceedings{zhuang-etal-2020-lit,
title = "{LIT} Team`s System Description for {J}apanese-{C}hinese Machine Translation Task in {IWSLT} 2020",
author = "Zhuang, Yimeng and
Zhang, Yuan and
Wang, Lijie",
editor = {Federico, Marcello and
Waibel, Alex and
Knight, Kevin and
Nakamura, Satoshi and
Ney, Hermann and
Niehues, Jan and
St{\"u}ker, Sebastian and
Wu, Dekai and
Mariani, Joseph and
Yvon, Francois},
booktitle = "Proceedings of the 17th International Conference on Spoken Language Translation",
month = jul,
year = "2020",
address = "Online",
publisher = "Association for Computational Linguistics",
url = "https://aclanthology.org/2020.iwslt-1.12/",
doi = "10.18653/v1/2020.iwslt-1.12",
pages = "109--113",
abstract = "This paper describes the LIT Team`s submission to the IWSLT2020 open domain translation task, focusing primarily on Japanese-to-Chinese translation direction. Our system is based on the organizers' baseline system, but we do more works on improving the Transform baseline system by elaborate data pre-processing. We manage to obtain significant improvements, and this paper aims to share some data processing experiences in this translation task. Large-scale back-translation on monolingual corpus is also investigated. In addition, we also try shared and exclusive word embeddings, compare different granularity of tokens like sub-word level. Our Japanese-to-Chinese translation system achieves a performance of BLEU=34.0 and ranks 2nd among all participating systems."
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<abstract>This paper describes the LIT Team‘s submission to the IWSLT2020 open domain translation task, focusing primarily on Japanese-to-Chinese translation direction. Our system is based on the organizers’ baseline system, but we do more works on improving the Transform baseline system by elaborate data pre-processing. We manage to obtain significant improvements, and this paper aims to share some data processing experiences in this translation task. Large-scale back-translation on monolingual corpus is also investigated. In addition, we also try shared and exclusive word embeddings, compare different granularity of tokens like sub-word level. Our Japanese-to-Chinese translation system achieves a performance of BLEU=34.0 and ranks 2nd among all participating systems.</abstract>
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%0 Conference Proceedings
%T LIT Team‘s System Description for Japanese-Chinese Machine Translation Task in IWSLT 2020
%A Zhuang, Yimeng
%A Zhang, Yuan
%A Wang, Lijie
%Y Federico, Marcello
%Y Waibel, Alex
%Y Knight, Kevin
%Y Nakamura, Satoshi
%Y Ney, Hermann
%Y Niehues, Jan
%Y Stüker, Sebastian
%Y Wu, Dekai
%Y Mariani, Joseph
%Y Yvon, Francois
%S Proceedings of the 17th International Conference on Spoken Language Translation
%D 2020
%8 July
%I Association for Computational Linguistics
%C Online
%F zhuang-etal-2020-lit
%X This paper describes the LIT Team‘s submission to the IWSLT2020 open domain translation task, focusing primarily on Japanese-to-Chinese translation direction. Our system is based on the organizers’ baseline system, but we do more works on improving the Transform baseline system by elaborate data pre-processing. We manage to obtain significant improvements, and this paper aims to share some data processing experiences in this translation task. Large-scale back-translation on monolingual corpus is also investigated. In addition, we also try shared and exclusive word embeddings, compare different granularity of tokens like sub-word level. Our Japanese-to-Chinese translation system achieves a performance of BLEU=34.0 and ranks 2nd among all participating systems.
%R 10.18653/v1/2020.iwslt-1.12
%U https://aclanthology.org/2020.iwslt-1.12/
%U https://doi.org/10.18653/v1/2020.iwslt-1.12
%P 109-113
Markdown (Informal)
[LIT Team’s System Description for Japanese-Chinese Machine Translation Task in IWSLT 2020](https://aclanthology.org/2020.iwslt-1.12/) (Zhuang et al., IWSLT 2020)
ACL