# Syllable-level Neural Language Model for Agglutinative Language

arXiv: https://arxiv.org/abs/1708.05515 · HTML: https://mlsanity.com/paper/1708.05515/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Aug 2017.

## Built on by (most cited first, 2 of 2)

- [Introducing Syllable Tokenization for Low-resource Languages: A Case Study with Swahili](https://mlsanity.com/paper/2406.15358/) (2024)
- [Revisiting Syllables in Language Modelling and their Application on Low-Resource Machine Translation](https://mlsanity.com/paper/2210.02509/) (2022)

## Abstract (arXiv)

Language models for agglutinative languages have always been hindered in past due to myriad of agglutinations possible to any given word through various affixes. We propose a method to diminish the problem of out-of-vocabulary words by introducing an embedding derived from syllables and morphemes which leverages the agglutinative property. Our model outperforms character-level embedding in perplexity by 16.87 with 9.50M parameters. Proposed method achieves state of the art performance over existing input prediction methods in terms of Key Stroke Saving and has been commercialized.