An overview of probabilistic tree transducers for natural language processing

Authors:
Kevin Knight;Jonathan Graehl
Affiliations:
Information Sciences Institute (ISI) and Computer Science Department, University of Southern California;Information Sciences Institute (ISI) and Computer Science Department, University of Southern California
Venue:
CICLing'05 Proceedings of the 6th international conference on Computational Linguistics and Intelligent Text Processing
Year:
2005

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Abstract

Probabilistic finite-state string transducers (FSTs) are extremely popular in natural language processing, due to powerful generic methods for applying, composing, and learning them. Unfortunately, FSTs are not a good fit for much of the current work on probabilistic modeling for machine translation, summarization, paraphrasing, and language modeling. These methods operate directly on trees, rather than strings. We show that tree acceptors and tree transducers subsume most of this work, and we discuss algorithms for realizing the same benefits found in probabilistic string transduction.