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Japanese--Russian--English News Commentary Parallel Data


This repository contains manually curated parallel sentences for Japanese--Russian, Japanese--English, and Russian--English language pairs in news domain.


The Japanese--Russian is one of the most distant language pairs and has only limited quantity of parallel data to train machine translation (MT) systems. To promote the research on low-resource MT, we have curated parallel sentences, which can be used as development and test data, through the following procedure:

  1. Downloaded from OPUS News Commentary data for Japanese--Russian with 586 sentence pairs and Japanese--English with 637 sentence pairs.
  2. The above Japanese--Russian and Japanese--English data share many lines in the Japanese side. Therefore, we first compiled a Russian--Japanese--English tri-text data.
  3. From each line, we identified corresponding parts across languages, and split off unaligned parts into a new line.
  4. As a result, we obtained 1,654 lines of data comprising trilingual, bilingual, and monolingual segments (mainly sentences).
  5. For the sake of comparability, we randomly chose 600 trilingual sentences to create a test set, and concatenated the rest of them and bilingual sentences to form development sets.

Distribution of tri-texts

Ru Ja En #sent Test Dev
913 600 313
- 173 - 173
- 276 - 276
- 0 - -
- - 4 - -
- - 287 - -
- - 1 - -

Development and test splits (available in this repository)

L1--L2 Development Test
Japanese--Russian 486 600
Japanese--English 589 600
Russian--English 313 600


Scoreboard (BLEU-cased)

System description Resources Used Ja-to-Ru Ru-to-Ja
Uni-directional Transformer NMT (a) 0.70 1.96
Multi-to-multi Transformer NMT involving English (a) 3.72 8.35
Same but with multi-lingual multi-stage fine-tuning (a) (b) (c) (d) 7.49 12.10

Data used for above systems are as follows:

(a) Global Voices parallel data retrieved from OPUS (v2015; included in this repository)

(b) ASPEC: Asian Scientific Paper Excerpt Corpus (out-of-domain Japanese--English parallel data)

(c) UN provided for WMT 18 (out-of-domain Russian--English parallel data)

(d) Yandex provided for WMT 18 (out-of-domain Russian--English parallel data)


  • Aizhan Imankulova, Raj Dabre, Atsushi Fujita, and Kenji Imamura. Exploiting Out-of-Domain Parallel Data through Multilingual Transfer Learning for Low-Resource Neural Machine Translation. In Proceedings of the 17th Machine Translation Summit (MT Summit), Aug., 2019. (to appear). arXiv


  • National Institute of Information and Communications Technology (henceforth, NICT) has made the database publicly available under the conditions of license specified below.
  • NICT bears no responsibility for the contents of the database and assumes no liability for any direct or indirect damage or loss whatsoever that may be incurred as a result of using the database.
  • If any copyright infringement or other problems are found in the database, please contact us at atsushi.fujita[at]nict[dot]go[dot]jp. We will review the issue and undertake appropriate measures when needed.


No claims of intellectual property are made on the work of preparation of the corpus. See the OPUS and/or CASMACAT for details.


The dataset has been developed as a part of work at Advanced Translation Technology Laboratory, Advanced Speech Translation Research and Development Promotion Center, National Institute of Information and Communications Technology.


Japanese--Russian--English News Commentary Parallel Data



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