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<Paper uid="P98-1041">
  <Title>Machine Translation vs. Dictionary Term Translation a Comparison for English-Japanese News Article Alignment</Title>
  <Section position="10" start_page="266" end_page="266" type="concl">
    <SectionTitle>
8 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have investigated the performance of MLIR with the DTL and MT models for news article alignment using English and Japanese texts. The results in this paper have shown surprisingly that MT does not have a clear advantage over the DTL model at all levels of recall. The trade-off between lexical transfer ambiguity and synonymy implies that we should seek a middle strategy: a sophisticated system would perhaps perform homonym disambiguation and then leave alternative synonyms in the translation query list. This should maximise both precision and recall and will be a target for our future work. Furthermore, we would like to extend our investigation to other MLIR test sets to see how MT performs against DTL when the number of terms in the query is smaller.</Paragraph>
  </Section>
class="xml-element"></Paper>