We propose the use of WordNet synsets
in a syntax-based reordering model for hierarchical
statistical machine translation
(HPB-SMT) to enable the model to generalize
to phrases not seen in the training
data but that have equivalent meaning.
We detail our methodology to incorporate
synsets’ knowledge in the reordering
model and evaluate the resulting WordNetenhanced
SMT systems on the English-toFarsi
language direction. The inclusion of
synsets leads to the best BLEU score, outperforming
the baseline (standard HPBSMT)
by 0.6 points absolute.
We propose the use of WordNet synsets
in a syntax-based reordering model for hierarchical
statistical machine translation
(HPB-SMT) to enable the model to generalize
to phrases not seen in the training
data but that have equivalent meaning.
We detail our methodology to incorporate
synsets’ knowledge in the reordering
model and evaluate the resulting WordNetenhanced
SMT systems on the English-toFarsi
language direction. The inclusion of
synsets leads to the best BLEU score, outperforming
the baseline (standard HPBSMT)
by 0.6 points absolute.