Arefeh Kazemi


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Using Wordnet to Improve Reordering in Hierarchical Phrase-Based Statistical Machine Translation
Arefeh Kazemi | Antonio Toral | Andy Way
Proceedings of the 8th Global WordNet Conference (GWC)

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 WordNet-enhanced SMT systems on the English-to-Farsi language direction. The inclusion of synsets leads to the best BLEU score, outperforming the baseline (standard HPB-SMT) by 0.6 points absolute.


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Dependency-based Reordering Model for Constituent Pairs in Hierarchical SMT
Arefeh Kazemi | Antonio Toral | Andy Way | Amirhassan Monadjemi | Mohammadali Nematbakhsh
Proceedings of the 18th Annual Conference of the European Association for Machine Translation