The JHU Machine Translation Systems for WMT 2018
We report on the efforts of the Johns Hopkins University to develop neural machine translation systems for the shared task for news translation organized around the Conference for Machine Translation (WMT) 2018. We developed systems for German{--}English, English{--} German, and Russian{--}English. Our novel contributions are iterative back-translation and fine-tuning on test sets from prior years.
Code (0)
등록된 구현이 없습니다.
Tasks
Machine TranslationTranslationSimilar Papers 제목 키워드 기반
Opportunities for Human-centered Evaluation of Machine Translation Systems
Machine translation models are embedded in larger user-facing systems. Although model evaluation has matured, evaluation at the systems level is still lacking. We review literature from both the translation studies and H…
Machine TranslationTranslationJUNLP@ICON2020: Low Resourced Machine Translation for Indic Languages
In the current work, we present the description of the systems submitted to a machine translation shared task organized by ICON 2020: 17th International Conference on Natural Language Processing. The systems were develop…
Machine TranslationTranslationAdaptation and Combination of NMT Systems: The KIT Translation Systems for IWSLT 2016
In this paper, we present the KIT systems of the IWSLT 2016 machine translation evaluation. We participated in the machine translation (MT) task as well as the spoken language language translation (SLT) track for English…
Domain AdaptationMachine TranslationNMTTranslationNeural-based machine translation for medical text domain. Based on European Medicines Agency leaflet texts
The quality of machine translation is rapidly evolving. Today one can find several machine translation systems on the web that provide reasonable translations, although the systems are not perfect. In some specific domai…
DecoderMachine TranslationSentenceTranslationByte-based Neural Machine Translation
This paper presents experiments comparing character-based and byte-based neural machine translation systems. The main motivation of the byte-based neural machine translation system is to build multi-lingual neural machin…
Language ModelingLanguage ModellingMachine TranslationNamed Entity Recognition (NER)+2