| Introduction | Statistical machine translation into a morphologically complex language such as Turkish, Finnish or Arabic, involves the generation of target words with the proper morphology, in addition to properly ordering the target words. |
| Introduction | We assume that the reader is familiar with the basics of phrase-based statistical machine translation (Koehn et al., 2003) and factored statistical machine translation (Koehn and Hoang, 2007). |
| Related Work | Statistical Machine Translation into a morphologically rich language is a challenging problem in that, on the target side, the decoder needs to generate both the right sequence of constituents and the right sequence of morphemes for each word. |
| Related Work | Using morphology in statistical machine translation has been addressed by many researchers for translation from or into morphologically rich(er) languages. |
| Related Work | Goldwater and McClosky (2005) use morphological analysis on the Czech side to get improvements in Czech-to-English statistical machine translation . |
| Abstract | We then apply the algorithms to statistical machine translation by computing the sense similarity between the source and target side of translation rule pairs. |
| Conclusions and Future Work | similarity for terms from parallel corpora and applied it to statistical machine translation . |
| Conclusions and Future Work | We have shown that the sense similarity computed between units from parallel corpora by means of our algorithm is helpful for at least one multilingual application: statistical machine translation . |
| Introduction | Is it useful for multilingual applications, such as statistical machine translation (SMT)? |
| Introduction | Second, we use the sense similarities between the source and target sides of a translation rule to improve statistical machine translation performance. |
| Abstract | This paper proposes to use monolingual collocations to improve Statistical Machine Translation (SMT). |
| Conclusion | Statistical Machine Translation . |
| Conclusion | The Mathematics of Statistical Machine Translation : Pa- |
| Conclusion | Moses: Open Source Toolkit for Statistical Machine Translation . |
| Abstract | Several attempts have been made to learn phrase translation probabilities for phrase-based statistical machine translation that go beyond pure counting of phrases in word-aligned training data. |
| Experimental Evaluation | We conducted our experiments on the German-English data published for the ACL 2008 Workshop on Statistical Machine Translation (WMT08). |
| Introduction | Europarl task from the ACL 2008 Workshop on Statistical Machine Translation (WMT08). |