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Old Age
Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
July 20, 2017 ยท Declared Dead ยท ๐ Conference on Empirical Methods in Natural Language Processing
"No code URL or promise found in abstract"
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Authors
Zhenisbek Assylbekov, Rustem Takhanov, Bagdat Myrzakhmetov, Jonathan N. Washington
arXiv ID
1707.06480
Category
cs.CL: Computation & Language
Cross-listed
cs.NE,
stat.ML
Citations
18
Venue
Conference on Empirical Methods in Natural Language Processing
Last Checked
4 months ago
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.
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