Work

The Edinburgh/JHU Phrase-based Machine Translation Systems for WMT~2015

Year: 2015

Type: conference-paper

Abstract: This paper describes the submission of the University of Edinburgh and the Johns Hopkins University for the shared translation task of the EMNLP 2015 Tenth Workshop on Statistical Machine Translation (WMT 2015).We set up phrase-based statistical machine translation systems for all ten language pairs of this year's evaluation campaign, which are English paired with Czech, Finnish, French, German, and Russian in both translation directions.Novel research directions we investigated include: neural network language models and bilingual neural network language models, a comprehensive use of word classes, and sparse lexicalized reordering features.

Source: Conference on Machine Translation (WMT)

DOI: 10.18653/v1/w15-3013

Authors: Barry Haddow, Matthias Huck, Alexandra Birch, Nikolay Bogoychev, Philipp Koehn

Institutions: University of Edinburgh, Johns Hopkins University

Language: English

Topic: Natural Language Processing Techniques

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

Open Access status: hybrid

Funder: European Commission

Awards: Translation for Massive Open Online Courses, QT21: Quality Translation 21, Health in my Language, MMT will deliver a language independent commercial online translation service based on a new open-source machine translation distributed architecture