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

Zhenisbek Assylbekov, Rustem Takhanov, Bagdat Myrzakhmetov, Jonathan North Washington

2017

Full text Open access: Hybrid Metadata (CSL-JSON)
DOI 10.18653/v1/d17-1199
Data confidence 70 medium
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Abstract

Syllabification does not seem to improve word-level RNN language modeling quality when compared to characterbased 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 1.2-2.2times faster.