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<Paper uid="P05-2015">
  <Title>Learning Strategies for Open-Domain Natural Language Question Answering</Title>
  <Section position="5" start_page="89" end_page="89" type="concl">
    <SectionTitle>
4 Conclusion
</SectionTitle>
    <Paragraph position="0"> This paper present an approach to automatically learn strategies for natural language questions answering from examples composed of textual sources, questions, and corresponding answers.</Paragraph>
    <Paragraph position="1"> The strategies thus acquired are composed of ranked lists transformation rules that when applied to an initial state consisting of an unseen text and question, can derive the required answer. The model was shown to outperform three prior systems on a standard story comprehension corpus.</Paragraph>
  </Section>
class="xml-element"></Paper>