<?xml version="1.0" standalone="yes"?>
<Paper uid="P04-1077">
  <Title>Automatic Evaluation of Machine Translation Quality Using Longest Common Subsequence and Skip-Bigram Statistics</Title>
  <Section position="1" start_page="0" end_page="0" type="abstr">
    <SectionTitle>
Abstract
</SectionTitle>
    <Paragraph position="0"> In this paper we describe two new objective automatic evaluation methods for machine translation. The first method is based on longest common subsequence between a candidate translation and a set of reference translations.</Paragraph>
    <Paragraph position="1"> Longest common subsequence takes into account sentence level structure similarity naturally and identifies longest co-occurring in-sequence n-grams automatically. The second method relaxes strict n-gram matching to skip-bigram matching. Skip-bigram is any pair of words in their sentence order. Skip-bigram co-occurrence statistics measure the overlap of skip-bigrams between a candidate translation and a set of reference translations. The empirical results show that both methods correlate with human judgments very well in both adequacy and fluency.</Paragraph>
  </Section>
class="xml-element"></Paper>