Proceedings Of The 2017 Conference On Empirical Methods In Natural Language Processing
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.
Association For Computational Linguistics
September 7-11, 2017
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This work is licensed under a Creative Commons Attribution 4.0 License.
Z. Assylbekov, R. Takhanov, B. Myrzakhmetov, and Jonathan North Washington.
"Syllable-Aware Neural Language Models: A Failure To Beat Character-Aware Ones".
Proceedings Of The 2017 Conference On Empirical Methods In Natural Language Processing.