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Found 13 papers, 3 papers with code
Kyoto University MT System Description for IWSLT 2017
We describe here our Machine Translation (MT) model and the results we obtained for the IWSLT 2017 Multilingual Shared Task.
JASS: Japanese-specific Sequence to Sequence Pre-training for Neural Machine Translation
Monolingual pre-training approaches such as MASS (MAsked Sequence to Sequence) are extremely effective in boosting NMT quality for languages with small parallel corpora.
Kyoto University Participation to the WMT 2019 News Shared Task
We describe here the experiments we did for the the news translation shared task of WMT 2019.
Kyoto University Participation to WAT 2017
We describe here our approaches and results on the WAT 2017 shared translation tasks.
Neural Machine Translation: Basics, Practical Aspects and Recent Trends
Machine Translation (MT) is a sub-field of NLP which has experienced a number of paradigm shifts since its inception.
Enabling Multi-Source Neural Machine Translation By Concatenating Source Sentences In Multiple Languages
In this paper, we explore a simple solution to "Multi-Source Neural Machine Translation" (MSNMT) which only relies on preprocessing a N-way multilingual corpus without modifying the Neural Machine Translation (NMT) architecture or training procedure.
Kyoto University Participation to WAT 2016
We report very good translation results, especially when using neural MT for Chinese-to-Japanese translation.
