1,787 research outputs found
Results of the WMT15 Tuning Shared Task
This paper presents the results of the WMT15 Tuning Shared Task. We provided the
participants of this task with a complete machine translation system and asked them to tune its
internal parameters (feature weights). The tuned systems were used to translate the test set and
the outputs were manually ranked for translation quality. We received 4 submissions in the
English-Czech and 6 in the Czech-English translation direction. In addition, we ran
3 baseline setups, tuning the
parameters with standard optimizers for BLEU score
Benchmarking SMT performance for Farsi using the TEP++ Corpus
Statistical machine translation (SMT) suffers from various problems which are exacerbated where training data is in short
supply. In this paper we address the data
sparsity problem in the Farsi (Persian) language and introduce a new parallel corpus, TEP++. Compared to previous results the new dataset is more efficient for
Farsi SMT engines and yields better output. In our experiments using TEP++ as
bilingual training data and BLEU as a metric, we achieved improvements of +11.17
(60%) and +7.76 (63.92%) in the Farsi–
English and English–Farsi directions, respectively. Furthermore we describe an
engine (SF2FF) to translate between formal and informal Farsi which in terms of
syntax and terminology can be seen as
different languages. The SF2FF engine
also works as an intelligent normalizer for
Farsi texts. To demonstrate its use, SF2FF
was used to clean the IWSLT–2013 dataset
to produce normalized data, which gave
improvements in translation quality over
FBK’s Farsi engine when used as training
dat
Syntax and Rich Morphology in MT
The talk describes in detail the issues specific to English-to-Czech MT: sentence syntax and target-side rich morphology
Proceedings of the 17th Annual Conference of the European Association for Machine Translation
Proceedings of the 17th Annual Conference of the European Association for Machine Translation (EAMT
Proceedings
Proceedings of the NODALIDA 2011 Workshop
Constraint Grammar Applications.
Editors: Eckhard Bick, Kristin Hagen, Kaili Müürisep, Trond Trosterud.
NEALT Proceedings Series, Vol. 14 (2011), vi+69 pp.
© 2011 The editors and contributors.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/19231
Domain adaptation for statistical machine translation of corporate and user-generated content
The growing popularity of Statistical Machine Translation (SMT) techniques in recent years has led to the development of multiple domain-specic resources and adaptation scenarios. In this thesis we address two important and industrially relevant adaptation scenarios, each suited to different kinds of content.
Initially focussing on professionally edited `enterprise-quality' corporate content, we address a specic scenario of data translation from a mixture of different domains where, for each of them domain-specific data is available. We utilise an automatic classifier to combine multiple domain-specific models and empirically show that such a configuration results in better translation quality compared to both traditional and state-of-the-art techniques for handling mixed domain translation.
In the second phase of our research we shift our focus to the translation of possibly `noisy' user-generated content in web-forums created around products and services of a multinational company. Using professionally edited translation memory (TM) data for training, we use different normalisation and data selection techniques to adapt SMT models to noisy forum content. In this scenario, we also study the effect of mixture adaptation using a combination of in-domain and out-of-domain data at different component levels of an SMT system. Finally we focus on the task of optimal supplementary training data selection from out-of-domain corpora using a novel incremental model merging mechanism to adapt TM-based models to improve forum-content translation quality
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