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<Paper uid="N03-1019">
  <Title>A Weighted Finite State Transducer Implementation of the Alignment Template Model for Statistical Machine Translation</Title>
  <Section position="7" start_page="0" end_page="0" type="concl">
    <SectionTitle>
5 Discussion
</SectionTitle>
    <Paragraph position="0"> The main motivation for our investigation into this WFST modeling framework for statistical machine translation lies in the simplicity of the alignment and translation processes relative to other dynamic programming or a2a4a3 decoders (Och, 2002). Once the components of the alignment template translation model are implemented as WF-STs, alignment and translation can be performed using standard FSM operations that have already been implemented and optimized. It is not necessary to develop specialized search procedures, even for the generation of lattices and N-best lists of alignment and translation alternatives. null The derivation of the ATTM was presented with the intent of clearly identifying the conditional independence assumptions that underly the WFST implementation.</Paragraph>
    <Paragraph position="1"> This approach leads to modular implementations of the component distributions of the translation model. These components can be refined and improved by changing the corresponding transducers without requiring changes to the overall search procedure. However some of the modeling assumptions are extremely strong. We note in particular that segmentation and translation are carried out independently in that phrase segmentation is followed by phrasal translation; performing these steps independently can easily lead to search errors.</Paragraph>
    <Paragraph position="2"> It is a strength of the ATTM that it can be directly constructed from available bitext word alignments. However this construction should only be considered an initialization of the ATTM model parameters. Alignment and translation can be expected to improve as the model is refined and in future work we will investigate iterative parameter estimation procedures.</Paragraph>
    <Paragraph position="3"> We have presented a novel approach to generate alignments and alignment lattices under the ATTM. These lattices will likely be very helpful in developing ATTM parameter estimation procedures, in that they can be used to provide conditional distributions over the latent model variables. We have observed that that poor coverage of the test set by the template library may be why the over-all word alignments produced by the ATTM are relatively poor; we will therefore also explore new strategies for template selection.</Paragraph>
    <Paragraph position="4"> The alignment template model is a powerful modeling framework for statistical machine translation. It is our goal to improve its performance through new training procedures while refining the basic WFST architecture.</Paragraph>
  </Section>
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