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Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones

20 July 2017
Z. Assylbekov
Rustem Takhanov
Bagdat Myrzakhmetov
Jonathan North Washington
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Abstract

Syllabification does not seem to improve word-level RNN language modeling quality when compared to character-based segmentation. However, our best syllable-aware language model, achieving performance comparable to the competitive character-aware model, has 18%-33% fewer parameters and is trained 1.2-2.2 times faster.

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