4,123 research outputs found
Experiments on domain adaptation for English-Hindi SMT
Statistical Machine Translation (SMT) systems are usually trained on large amounts of bilingual text and monolingual target language text. If a significant amount of out-of-domain data is added to the training data, the quality of translation can drop. On the other hand, training an SMT system on a small amount of training material for given indomain data leads to narrow lexical coverage which again results in a low translation quality. In this paper, (i) we explore domain-adaptation techniques to combine large out-of-domain training data with small-scale in-domain training data for EnglishâHindi statistical machine translation and (ii) we cluster large out-of-domain training data to extract sentences similar to in-domain sentences and apply adaptation techniques to combine clustered sub-corpora
with in-domain training data into a unified framework, achieving a 0.44 absolute corresponding to a 4.03% relative improvement in terms of BLEU over the baseline
Experiments on domain adaptation for patent machine translation in the PLuTO project
The PLUTO1 project (Patent Language Translations Online) aims to provide a rapid solution for the online retrieval and translation of patent documents through the integration of a number of existing state-of-the-art components provided by the project partners. The paper presents some of the experiments on patent domain adaptation of the Machine Translation (MT) systems used in the PLuTO project. The experiments use the International Patent Classification for domain adaptation and are focused on the EnglishâFrench language pair
A discriminative latent variable-based "DE" classifier for ChineseâEnglish SMT
Syntactic reordering on the source-side
is an effective way of handling word order
differences. The (DE) construction
is a flexible and ubiquitous syntactic
structure in Chinese which is a major
source of error in translation quality.
In this paper, we propose a new classifier
model â discriminative latent variable
model (DPLVM) â to classify the
DE construction to improve the accuracy
of the classification and hence the translation
quality. We also propose a new feature
which can automatically learn the reordering
rules to a certain extent. The experimental
results show that the MT systems
using the data reordered by our proposed
model outperform the baseline systems
by 6.42% and 3.08% relative points
in terms of the BLEU score on PB-SMT
and hierarchical phrase-based MT respectively.
In addition, we analyse the impact
of DE annotation on word alignment and
on the SMT phrase table
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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