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A phrase-based hidden semi-markov approach to machine translation
Jesús Andrés-Ferrer and Alfons Juan
In: 13th Annual Meeting of the European Association for Machine Translation, 14-15 May 2009, Barcelona, Spain.
AbstractStatistically estimated phrase-based models
promised to further the state-of-the-art,
however, several works have shown that
they behave worse than heuristically estimated
phrase-based models. In this work
we present a latent variable phrase-based
translation model inspired on the hidden
semi-Markov models, that does not degrade
the system performance. Results
show that this model incurs in an improvement
over the baseline. Additionally, we
show that both Baum-Welch and Viterbi
training obtain the very same result, suggesting
that the model gathers most of the
probability mass into one bilingual segmentation. [Edit]
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