Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 20 Jul 2017.
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Citing papers per year: 2021: 2; 2022: 1.
3 papers.
- 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Surveycited by 46
- 2021Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Languagecited by 0
- 2022Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translationcited by 0
- 2022Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation5 Oct 2022
- 2021Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language25 Oct 2021
- 2021Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey14 Apr 2021