@inproceedings{kocmi-etal-2017-cuni,
title = "{CUNI} {NMT} System for {WAT} 2017 Translation Tasks",
author = "Kocmi, Tom and
Vari{\v{s}}, Du{\v{s}}an and
Bojar, Ond{\v{r}}ej",
editor = "Nakazawa, Toshiaki and
Goto, Isao",
booktitle = "Proceedings of the 4th Workshop on {A}sian Translation ({WAT}2017)",
month = nov,
year = "2017",
address = "Taipei, Taiwan",
publisher = "Asian Federation of Natural Language Processing",
url = "https://aclanthology.org/W17-5715",
pages = "154--159",
abstract = "The paper presents this year{'}s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask. We compare two neural network architectures, the standard sequence-to-sequence with attention (Seq2Seq) and an architecture using convolutional sentence encoder (FBConv2Seq), both implemented in the NMT framework Neural Monkey that we currently participate in developing. We also compare various types of preprocessing of the source Japanese sentences and their impact on the overall results. Furthermore, we include the results of our experiments with out-of-domain data obtained by combining the corpora provided for each subtask.",
}
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%0 Conference Proceedings
%T CUNI NMT System for WAT 2017 Translation Tasks
%A Kocmi, Tom
%A Variš, Dušan
%A Bojar, Ondřej
%Y Nakazawa, Toshiaki
%Y Goto, Isao
%S Proceedings of the 4th Workshop on Asian Translation (WAT2017)
%D 2017
%8 November
%I Asian Federation of Natural Language Processing
%C Taipei, Taiwan
%F kocmi-etal-2017-cuni
%X The paper presents this year’s CUNI submissions to the WAT 2017 Translation Task focusing on the Japanese-English translation, namely Scientific papers subtask, Patents subtask and Newswire subtask. We compare two neural network architectures, the standard sequence-to-sequence with attention (Seq2Seq) and an architecture using convolutional sentence encoder (FBConv2Seq), both implemented in the NMT framework Neural Monkey that we currently participate in developing. We also compare various types of preprocessing of the source Japanese sentences and their impact on the overall results. Furthermore, we include the results of our experiments with out-of-domain data obtained by combining the corpora provided for each subtask.
%U https://aclanthology.org/W17-5715
%P 154-159
Markdown (Informal)
[CUNI NMT System for WAT 2017 Translation Tasks](https://aclanthology.org/W17-5715) (Kocmi et al., WAT 2017)
ACL
- Tom Kocmi, Dušan Variš, and Ondřej Bojar. 2017. CUNI NMT System for WAT 2017 Translation Tasks. In Proceedings of the 4th Workshop on Asian Translation (WAT2017), pages 154–159, Taipei, Taiwan. Asian Federation of Natural Language Processing.