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Learning-based Intrasentence Segmentation for Efficient Translation of Long Sentences

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Machine Translation

Abstract

Long-sentence analysis has been a critical problem in machine translation becauseof its high complexity. Intrasentence segmentation has been proposed as a methodfor reducing parsing complexity. This paper presents a two-step segmentation method:(1) identifying potential segmentation positions in a sentence and (2) selecting an actualsegmentation position amongst them. We have attempted to apply machine learningtechniques to the segmentation task: ``concept learning'' and ``genetic learning''. Bylearning the ``SegmentablePosition'' concept, the rules for identifying potentialsegmentation positions are postulated. The selection of the actual segmentationposition is based on a function whose parameters are determined by genetic learning.Experimental results are presented which illustrate the effectiveness of our approachto long-sentence parsing for MT. The results also show improved segmentationperformance in comparison to other existing methods.

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Authors and Affiliations

  1. Department of Computer Engineering, Hansung University, Samsun-dong, Sungbuk-gu, Seoul, Korea

    Sung-Dong Kim, Byoung-Tak Zhang & Yung Taek Kim

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  1. Sung-Dong Kim
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  2. Byoung-Tak Zhang
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  3. Yung Taek Kim
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Kim, SD., Zhang, BT. & Kim, Y.T. Learning-based Intrasentence Segmentation for Efficient Translation of Long Sentences. Machine Translation 16, 151–174 (2001). https://doi.org/10.1023/A:1019896420277

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