The impact of parse quality on syntactically-informed statistical machine translation

We investigate the impact of parse quality on a syntactically-informed statistical machine translation system applied to technical text. We vary parse quality by varying the amount of data used to train the parser. As the amount of data increases, parse quality improves, leading to improvements in machine translation output and results that significantly outperform a state-of-the-art phrasal baseline.

• Type: Conference
• Year: 2006
• Where: EMNLP
• Authors: Chris Quirk, Simon Corston-Oliver
• Keywords: EMNLP 2006, EMNLP 2007, Machine Translation, Parse Quality, Statistical Machine Translation