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Capturing practical natural language transformations

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Machine Translation

Abstract

We study automata for capturing the transformations in practical natural language processing (NLP) systems, especially those that translate between human languages. For several variations of finite-state string and tree transducers, we survey answers to formal questions about their expressiveness, modularity, teachability, and generalization. We conclude that no formal device yet captures everything that is desirable, and we point to future research.

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Authors and Affiliations

  1. Information Sciences Institute, University of Southern California, 4676 Admiralty Way, Marina del Rey, CA, 90292, USA

    Kevin Knight

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  1. Kevin Knight
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Correspondence to Kevin Knight.

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Knight, K. Capturing practical natural language transformations. Machine Translation 21, 121–133 (2007). https://doi.org/10.1007/s10590-008-9039-0

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  • DOI: https://doi.org/10.1007/s10590-008-9039-0

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