<?xml version="1.0" standalone="yes"?>
<Paper uid="N04-1033">
  <Title>Improvements in Phrase-Based Statistical Machine Translation</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In statistical machine translation, the currently best performing systems are based in some way on phrases or word groups. We describe the baseline phrase-based translation system and various refinements. We describe a highly efficient monotone search algorithm with a complexity linear in the input sentence length. We present translation results for three tasks: Verbmobil, Xerox and the Canadian Hansards. For the Xerox task, it takes less than 7 seconds to translate the whole test set consisting of more than 10K words. The translation results for the Xerox and Canadian Hansards task are very promising. The system even outperforms the alignment template system.</Paragraph>
  </Section>
class="xml-element"></Paper>