# Syllable-aware Neural Language Models: A Failure to Beat Character-aware Ones

arXiv: https://arxiv.org/abs/1707.06480 · HTML: https://mlsanity.com/paper/1707.06480/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 20 Jul 2017.

## Built on by (most cited first, 3 of 3)

- [Domain Adaptation and Multi-Domain Adaptation for Neural Machine Translation: A Survey](https://mlsanity.com/paper/2104.06951/) (2021)
- [Paradigm Shift in Language Modeling: Revisiting CNN for Modeling Sanskrit Originated Bengali and Hindi Language](https://mlsanity.com/paper/2110.13032/) (2021)
- [Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation](https://mlsanity.com/paper/2210.02509/) (2022)

## 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.