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<Paper uid="W05-0505">
  <Title>A Connectionist Model of Language-Scene Interaction</Title>
  <Section position="7" start_page="43" end_page="43" type="concl">
    <SectionTitle>
5 Conclusion
</SectionTitle>
    <Paragraph position="0"> We have presented a neural network architecture that successfully models the results of five recent experiments designed to study the interaction of visual context with sentence processing. The model shows that it can adaptively use information from the visual scene such as depicted events, when present, to anticipate roles and fillers as observed in each of the experiments, as well as demonstrate traditional incremental processing when context is absent. Furthermore, more recent results show that training the network in a visual environment, with stereotypical knowledge gradually learned and reinforced, allows the model to negotiate even conflicting information sources.</Paragraph>
  </Section>
class="xml-element"></Paper>