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        <title>Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones</title>
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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.</abstract>
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%0 Conference Proceedings
%T Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
%A Assylbekov, Zhenisbek
%A Takhanov, Rustem
%A Myrzakhmetov, Bagdat
%A Washington, Jonathan N.
%Y Palmer, Martha
%Y Hwa, Rebecca
%Y Riedel, Sebastian
%S Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing
%D 2017
%8 September
%I Association for Computational Linguistics
%C Copenhagen, Denmark
%F assylbekov-etal-2017-syllable
%X 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.
%R 10.18653/v1/D17-1199
%U https://aclanthology.org/D17-1199
%U https://doi.org/10.18653/v1/D17-1199
%P 1866-1872

Markdown (Informal)

[Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones](https://aclanthology.org/D17-1199) (Assylbekov et al., EMNLP 2017)

• Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones (Assylbekov et al., EMNLP 2017)

ACL

• Zhenisbek Assylbekov, Rustem Takhanov, Bagdat Myrzakhmetov, and Jonathan N. Washington. 2017. Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones. In Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing, pages 1866–1872, Copenhagen, Denmark. Association for Computational Linguistics.