Syllable-level Neural Language Model for Agglutinative Language

doi:10.18653/v1/W17-4113

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Syllable-level Neural Language Model for Agglutinative Language

Seunghak Yu, Nilesh Kulkarni, Haejun Lee

Aug 1, 2017

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Key Takeaway

Key Takeaway: Our novel embedding method for agglutinative languages improves input prediction performance and achieves state-of-the-art Key Stroke Saving compared to existing methods.

Abstract

We introduce a novel method to diminish the problem of out of vocabulary words by introducing an embedding method which leverages the agglutinative property of language. We propose additional embedding derived from syllables and morphemes for the words to improve the performance of language model. We apply the above method to input prediction tasks and achieve state of the art performance in terms of Key Stroke Saving (KSS) w.r.t. to existing device input prediction methods.

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