A Simple and Strong Baseline: NAIST-NICT Neural Machine Translation System for WAT2017 English-Japanese Translation Task

نویسندگان

  • Yusuke Oda
  • Katsuhito Sudoh
  • Satoshi Nakamura
  • Masao Utiyama
  • Eiichiro Sumita
چکیده

This paper describes the details about the NAIST-NICT machine translation system for WAT2017 English-Japanese Scientific Paper Translation Task. The system consists of a language-independent tokenizer and an attentional encoder-decoder style neural machine translation model. According to the official results, our system achieves higher translation accuracy than any systems submitted previous campaigns despite simple model architecture.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

NICT-NAIST System for WMT17 Multimodal Translation Task

This paper describes the NICT-NAIST system for the WMT 2017 shared multimodal machine translation task for both language pairs, English-to-German and English-to-French. We built a hierarchical phrase-based (Hiero) translation system and trained an attentional encoder-decoder neural machine translation (NMT) model to rerank the n-best output of the Hiero system, which obtained significant gains ...

متن کامل

Ensemble and Reranking: Using Multiple Models in the NICT-2 Neural Machine Translation System at WAT2017

In this paper, we describe the NICT-2 neural machine translation system evaluated at WAT2017. This system uses multiple models as an ensemble and combines models with opposite decoding directions by reranking (called bi-directional reranking). In our experimental results on small data sets, the translation quality improved when the number of models was increased to 32 in total and did not satur...

متن کامل

Lexicons and Minimum Risk Training for Neural Machine Translation: NAIST-CMU at WAT2016

This year, the Nara Institute of Science and Technology (NAIST)/Carnegie Mellon University (CMU) submission to the Japanese-English translation track of the 2016 Workshop on Asian Translation was based on attentional neural machine translation (NMT) models. In addition to the standard NMT model, we make a number of improvements, most notably the use of discrete translation lexicons to improve p...

متن کامل

NICT at WAT 2015

Translation systems of our NICT team at the 2nd Workshop on Asian Translation (WAT 2015) are described in this paper. We participated in two translation tasks: Japanese-to-English (JE) and Korean-toJapanese (KJ). A baseline phrased-based (PB) statistical machine translation (SMT) system in Moses was used. On JE translation, two pre-reordering approaches were applied: a simple reverse preorderin...

متن کامل

NICT@WMT09: Model Adaptation and Transliteration for Spanish-English SMT

This paper describes the NICT statistical machine translation (SMT) system used for the WMT 2009 Shared Task (WMT09) evaluation. We participated in the Spanish-English translation task. The focus of this year’s participation was to investigate model adaptation and transliteration techniques in order to improve the translation quality of the baseline phrasebased SMT system.

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2017