24 research outputs found
The QT21 Combined Machine Translation System for English to Latvian
This paper describes the joint submis-
sion of the QT21 projects for the
English
â
Latvian translation task of the
EMNLP 2017 Second Conference on Ma-
chine Translation
(WMT 2017). The sub-
mission is a system combination which
combines seven different statistical ma-
chine translation systems provided by the
different groups.
The systems are combined using either
RWTHâs system combination approach,
or
USFDâs
consensus-based
system-
selection approach. The final submission
shows an improvement of 0.5 B
LEU
compared to the best single system on
newstest2017
Findings of the 2017 Conference on Machine Translation
This paper presents the results of the
WMT17 shared tasks, which included
three machine translation (MT) tasks
(news, biomedical, and multimodal), two
evaluation tasks (metrics and run-time estimation
of MT quality), an automatic
post-editing task, a neural MT training
task, and a bandit learning task
The University of Edinburghâs Neural MT Systems for WMT17
This paper describes the University of Edinburgh's submissions to the WMT17
shared news translation and biomedical translation tasks. We participated in 12
translation directions for news, translating between English and Czech, German,
Latvian, Russian, Turkish and Chinese. For the biomedical task we submitted
systems for English to Czech, German, Polish and Romanian. Our systems are
neural machine translation systems trained with Nematus, an attentional
encoder-decoder. We follow our setup from last year and build BPE-based models
with parallel and back-translated monolingual training data. Novelties this
year include the use of deep architectures, layer normalization, and more
compact models due to weight tying and improvements in BPE segmentations. We
perform extensive ablative experiments, reporting on the effectivenes of layer
normalization, deep architectures, and different ensembling techniques.Comment: WMT 2017 shared task track; for Bibtex, see
http://homepages.inf.ed.ac.uk/rsennric/bib.html#uedin-nmt:201
Translation quality and productivity: a study on rich morphology languages
© 2017 The Authors. Published by Asia-Pacific Association for Machine Translation. This is an open access article available under a Creative Commons licence.
The published version can be accessed at the following link on the publisherâs website: http://aamt.info/app-def/S-102/mtsummit/2017/wp-content/uploads/sites/2/2017/09/MTSummitXVI_ResearchTrack.pdfSpecia, L., Blain, F., Harris, K., Burchardt, A. et al. (2017) Translation quality and productivity: a study on rich morphology languages. In, Machine Translation Summit XVI, Vol 1. MT Research Track, Kurohashi, S., and Fung, P., Nagoya, Aichi, Japan: Asia-Pacific Association for Machine Translation, pp. 55-71.This work was supported by the QT21 project (H2020 No. 645452)
Findings of the 2017 Conference on Machine Translation (WMT17)
This paper presents the results of theWMT17 shared tasks, which included three machine translation (MT) tasks(news, biomedical, and multimodal), two evaluation tasks (metrics and run-time estimation of MT quality), an automatic post-editing task, a neural MT training task, and a bandit learning task
Translation Quality and Productivity: A Study on Rich Morphology Languages.
This paper introduces a unique large-scale machine translation dataset with various levels of human annotation combined with automatically recorded productivity features such as time and keystroke logging and manual scoring during the annotation process. The data was collected as part of the EU-funded QT21 project and comprises 20,000â45,000 sentences of industry-generated content with translation into English and three morphologically rich languages: EnglishâGerman/Latvian/Czech and GermanâEnglish, in either the information technologyor life sciences domain. Altogether, the data consists of 176,476 tuples including a sourcesentence, the respective machine translation by a statistical system (additionally, by a neural system for two language pairs), a post-edited version of such translation by a native-speaking professional translator, an independently created reference translation, and information on post-editing: time, keystrokes, Likert scores, and annotator identifier. A subset of 2,000 sentences from this data per language pair and system type was also manually annotated with translation errors for deeper linguistic analysis. We describe the data collection process, provide a brief analysis of the resulting annotations and discuss the use of the data in quality estimation and automatic post-editing tasks
TectoMT â a deep-Âlinguistic core of the combined Chimera MT system
Chimera is a machine translation system that combines the TectoMT deep-linguistic core with phrase-based MT system Moses. For EnglishâCzech pair it also uses the Depfix post-correction system. All the components run on Unix/Linux platform and are open source (available from Perl repository CPAN and the LINDAT/CLARIN repository). The main website is https://ufal.mff.cuni.cz/tectomt. The development is currently supported by the QTLeap 7th FP project (http://qtleap.eu)
Results of the WMT17 metrics shared task
This paper presents the results of the
WMT17 Metrics Shared Task. We asked
participants of this task to score the outputs of the MT systems involved in the
WMT17 news translation task and Neural MT training task. We collected scores
of 14 metrics from 8 research groups. In
addition to that, we computed scores of
7 standard metrics (BLEU, SentBLEU,
NIST, WER, PER, TER and CDER) as
baselines. The collected scores were evaluated in terms of system-level correlation
(how well each metricâs scores correlate
with WMT17 official manual ranking of
systems) and in terms of segment level
correlation (how often a metric agrees with
humans in judging the quality of a particular sentence).
This year, we build upon two types of
manual judgements: direct assessment
(DA) and HUME manual semantic judgements