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

Computation and Language 2017-07-21 v1 Neural and Evolutionary Computing Machine Learning

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.

Keywords

Cite

@article{arxiv.1707.06480,
  title  = {Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones},
  author = {Zhenisbek Assylbekov and Rustem Takhanov and Bagdat Myrzakhmetov and Jonathan N. Washington},
  journal= {arXiv preprint arXiv:1707.06480},
  year   = {2017}
}

Comments

EMNLP 2017

R2 v1 2026-06-22T20:52:50.805Z