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.
Cite
CITATION STYLE
APA
Assylbekov, Z., Takhanov, R., Myrzakhmetov, B., & Washington, J. N. (2017). Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones. In EMNLP 2017 - Conference on Empirical Methods in Natural Language Processing, Proceedings (pp. 1866–1872). Association for Computational Linguistics (ACL). https://doi.org/10.18653/v1/d17-1199
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