4,718 research outputs found
The impact of source-side syntactic reordering on hierarchical phrase-based SMT
Syntactic reordering has been demonstrated
to be helpful and effective for handling
different word orders between source
and target languages in SMT. However, in
terms of hierarchial PB-SMT (HPB), does
the syntactic reordering still has a significant
impact on its performance? This
paper introduces a reordering approach
which explores the { (DE) grammatical
structure in Chinese. We employ
the Stanford DE classifier to recognise
the DE structures in both training and
test sentences of Chinese, and then perform
word reordering to make the Chinese
sentences better match the word order
of English. The annotated and reordered
training data and test data are applied
to a re-implemented HPB system and
the impact of the DE construction is examined.
The experiments are conducted
on the NIST 2008 evaluation data and experimental
results show that the BLEU
and METEOR scores are significantly improved
by 1.83/8.91 and 1.17/2.73 absolute/
relative points respectively
Comparing rule-based and data-driven approaches to Spanish-to-Basque machine translation
In this paper, we compare the rule-based and data-driven
approaches in the context of Spanish-to-Basque Machine Translation. The rule-based system we consider has been developed specifically for Spanish-to-Basque machine translation, and is tuned to this language pair. On the contrary, the data-driven system we use is generic, and has not been specifically designed to deal with Basque. Spanish-to-Basque Machine Translation is a challenge for data-driven
approaches for at least two reasons. First, there is lack of
bilingual data on which a data-driven MT system can be trained. Second, Basque is a morphologically-rich agglutinative language and translating to Basque requires a huge generation of morphological information, a difficult task for a generic system not specifically tuned to Basque. We present the results of a series of experiments, obtained on two different corpora, one being “in-domain” and the
other one “out-of-domain” with respect to the data-driven
system. We show that n-gram based automatic evaluation and edit-distance-based human evaluation yield two different sets of results. According to BLEU, the data-driven system outperforms the rule-based system on the in-domain data, while according to the human evaluation, the rule-based
approach achieves higher scores for both corpora
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