60,428 research outputs found
Wrapper syntax for example-based machine translation
TransBooster is a wrapper technology designed to improve the performance of wide-coverage machine translation
systems. Using linguistically motivated syntactic information, it automatically decomposes source language sentences into shorter and syntactically simpler chunks, and recomposes their translation to form target language sentences. This generally improves both the word order
and lexical selection of the translation. To date, TransBooster has been successfully applied to rule-based MT, statistical MT, and multi-engine MT. This paper presents
the application of TransBooster to Example-Based Machine Translation. In an experiment conducted on test sets
extracted from Europarl and the Penn II Treebank we show that our method can raise the BLEU score up to 3.8% relative
to the EBMT baseline. We also conduct a manual evaluation, showing that TransBooster-enhanced EBMT produces
a better output in terms of fluency than the baseline EBMT in 55% of the cases and in terms of accuracy in 53% of the
cases
C to O-O Translation: Beyond the Easy Stuff
Can we reuse some of the huge code-base developed in C to take advantage of
modern programming language features such as type safety, object-orientation,
and contracts? This paper presents a source-to-source translation of C code
into Eiffel, a modern object-oriented programming language, and the supporting
tool C2Eif. The translation is completely automatic and supports the entire C
language (ANSI, as well as many GNU C Compiler extensions, through CIL) as used
in practice, including its usage of native system libraries and inlined
assembly code. Our experiments show that C2Eif can handle C applications and
libraries of significant size (such as vim and libgsl), as well as challenging
benchmarks such as the GCC torture tests. The produced Eiffel code is
functionally equivalent to the original C code, and takes advantage of some of
Eiffel's object-oriented features to produce safe and easy-to-debug
translations
Kranc: a Mathematica application to generate numerical codes for tensorial evolution equations
We present a suite of Mathematica-based computer-algebra packages, termed
"Kranc", which comprise a toolbox to convert (tensorial) systems of partial
differential evolution equations to parallelized C or Fortran code. Kranc can
be used as a "rapid prototyping" system for physicists or mathematicians
handling very complicated systems of partial differential equations, but
through integration into the Cactus computational toolkit we can also produce
efficient parallelized production codes. Our work is motivated by the field of
numerical relativity, where Kranc is used as a research tool by the authors. In
this paper we describe the design and implementation of both the Mathematica
packages and the resulting code, we discuss some example applications, and
provide results on the performance of an example numerical code for the
Einstein equations.Comment: 24 pages, 1 figure. Corresponds to journal versio
Learning with Latent Language
The named concepts and compositional operators present in natural language
provide a rich source of information about the kinds of abstractions humans use
to navigate the world. Can this linguistic background knowledge improve the
generality and efficiency of learned classifiers and control policies? This
paper aims to show that using the space of natural language strings as a
parameter space is an effective way to capture natural task structure. In a
pretraining phase, we learn a language interpretation model that transforms
inputs (e.g. images) into outputs (e.g. labels) given natural language
descriptions. To learn a new concept (e.g. a classifier), we search directly in
the space of descriptions to minimize the interpreter's loss on training
examples. Crucially, our models do not require language data to learn these
concepts: language is used only in pretraining to impose structure on
subsequent learning. Results on image classification, text editing, and
reinforcement learning show that, in all settings, models with a linguistic
parameterization outperform those without
Contextual bitext-derived paraphrases in automatic MT evaluation
In this paper we present a novel method for deriving paraphrases during automatic MT evaluation using only the source and reference texts, which are necessary for
the evaluation, and word and phrase alignment software. Using target language paraphrases produced through word and
phrase alignment a number of alternative reference sentences are constructed automatically for each candidate translation. The method produces lexical and lowlevel
syntactic paraphrases that are relevant to the domain in hand, does not use external knowledge resources, and can be
combined with a variety of automatic MT evaluation system
A syntactic skeleton for statistical machine translation
We present a method for improving statistical machine translation performance by using linguistically motivated syntactic information. Our algorithm recursively decomposes source language sentences into syntactically simpler and shorter chunks, and recomposes their translation to form target language sentences. This improves both the word order and lexical selection of the translation. We report statistically significant relative improvementsof 3.3% BLEU score in an experiment (English!Spanish) carried out on
an 800-sentence test set extracted from the Europarl corpus
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