100 research outputs found
An Open Source Toolkit for Word-level Confidence Estimation in Machine Translation
International audienceRecently, a growing need of Confidence Estimation (CE) for Statistical Machine Translation (SMT) systems in Computer Aided Translation (CAT), was observed. However, most of the CE toolkits are optimized for a single target language (mainly English) and, as far as we know, none of them are dedicated to this specific task and freely available. This paper presents an open-source toolkit for predicting the quality of words of a SMT output, whose novel contributions are (i) support for various target languages, (ii) handle a number of features of different types (system-based, lexical , syntactic and semantic). In addition, the toolkit also integrates a wide variety of Natural Language Processing or Machine Learning tools to pre-process data, extract features and estimate confidence at word-level. Features for Word-level Confidence Estimation (WCE) can be easily added / removed using a configuration file. We validate the toolkit by experimenting in the WCE evaluation framework of WMT shared task with two language pairs: French-English and English-Spanish. The toolkit is made available to the research community with ready-made scripts to launch full experiments on these language pairs, while achieving state-of-the-art and reproducible performances
Findings of the 2015 Workshop on Statistical Machine Translation
This paper presents the results of the
WMT15 shared tasks, which included a
standard news translation task, a metrics
task, a tuning task, a task for run-time
estimation of machine translation quality,
and an automatic post-editing task. This
year, 68 machine translation systems from
24 institutions were submitted to the ten
translation directions in the standard translation
task. An additional 7 anonymized
systems were included, and were then
evaluated both automatically and manually.
The quality estimation task had three
subtasks, with a total of 10 teams, submitting
34 entries. The pilot automatic postediting
task had a total of 4 teams, submitting
7 entries
Findings of the 2014 Workshop on Statistical Machine Translation
This paper presents the results of the
WMT14 shared tasks, which included a
standard news translation task, a separate
medical translation task, a task for
run-time estimation of machine translation
quality, and a metrics task. This year, 143
machine translation systems from 23 institutions
were submitted to the ten translation
directions in the standard translation
task. An additional 6 anonymized systems
were included, and were then evaluated
both automatically and manually. The
quality estimation task had four subtasks,
with a total of 10 teams, submitting 57 entries
CUNI in WMT14: Chimera Still Awaits Bellerophon
We present our English→Czech and
English→Hindi submissions for this
year’s WMT translation task. For
English→Czech, we build upon last year’s
CHIMERA and evaluate several setups.
English→Hindi is a new language pair for
this year. We experimented with reverse
self-training to acquire more (synthetic)
parallel data and with modeling target-side
morphology
From feature to paradigm: deep learning in machine translation
In the last years, deep learning algorithms have highly revolutionized several areas including speech, image and natural language processing. The specific field of Machine Translation (MT) has not remained invariant. Integration of deep learning in MT varies from re-modeling existing features into standard statistical systems to the development of a new architecture. Among the different neural networks, research works use feed- forward neural networks, recurrent neural networks and the encoder-decoder schema. These architectures are able to tackle challenges as having low-resources or morphology variations. This manuscript focuses on describing how these neural networks have been integrated to enhance different aspects and models from statistical MT, including language modeling, word alignment, translation, reordering, and rescoring. Then, we report the new neural MT approach together with a description of the foundational related works and recent approaches on using subword, characters and training with multilingual languages, among others. Finally, we include an analysis of the corresponding challenges and future work in using deep learning in MTPostprint (author's final draft
Machine Translation of Medical Texts in the Khresmoi Project
The WMT 2014 Medical Translation Task poses an interesting challenge for Machine Translation
(MT). In the standard translation task, the end application is the translation itself. In this task, the MT system is considered a part of a larger system for cross-lingual information retrieval (IR)
Edinburgh’s Phrase-based Machine Translation Systems for WMT-14
Abstract This paper describes the University of Edinburgh's (UEDIN) phrase-based submissions to the translation and medical translation shared tasks of the 2014 Workshop on Statistical Machine Translation (WMT). We participated in all language pairs. We have improved upon our 2013 system by i) using generalized representations, specifically automatic word clusters for translations out of English, ii) using unsupervised character-based models to translate unknown words in RussianEnglish and Hindi-English pairs, iii) synthesizing Hindi data from closely-related Urdu data, and iv) building huge language on the common crawl corpus. Translation Task Our baseline systems are based on the setup described in Baseline We trained our systems with the following settings: a maximum sentence length of 80, growdiag-final-and symmetrization of GIZA++ alignments, an interpolated Kneser-Ney smoothed 5-gram language model with KenLM (Heafield, 2011) The systems were tuned on a very large tuning set consisting of the test sets from 2008-2012, with a total of 13,071 sentences. We used newstest 2013 for the dev experiments. For RussianEnglish pairs news-test 2012 was used for tuning and for Hindi-English pairs, we divided the newsdev 2014 into two halves, used the first half for tuning and second for dev experiments. Using Generalized Word Representations We explored the use of automatic word clusters in phrase-based model
A Survey of Word Reordering in Statistical Machine Translation: Computational Models and Language Phenomena
Word reordering is one of the most difficult aspects of statistical machine
translation (SMT), and an important factor of its quality and efficiency.
Despite the vast amount of research published to date, the interest of the
community in this problem has not decreased, and no single method appears to be
strongly dominant across language pairs. Instead, the choice of the optimal
approach for a new translation task still seems to be mostly driven by
empirical trials. To orientate the reader in this vast and complex research
area, we present a comprehensive survey of word reordering viewed as a
statistical modeling challenge and as a natural language phenomenon. The survey
describes in detail how word reordering is modeled within different
string-based and tree-based SMT frameworks and as a stand-alone task, including
systematic overviews of the literature in advanced reordering modeling. We then
question why some approaches are more successful than others in different
language pairs. We argue that, besides measuring the amount of reordering, it
is important to understand which kinds of reordering occur in a given language
pair. To this end, we conduct a qualitative analysis of word reordering
phenomena in a diverse sample of language pairs, based on a large collection of
linguistic knowledge. Empirical results in the SMT literature are shown to
support the hypothesis that a few linguistic facts can be very useful to
anticipate the reordering characteristics of a language pair and to select the
SMT framework that best suits them.Comment: 44 pages, to appear in Computational Linguistic
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