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Language modelling is regularly analysed at word, subword or character units,but syllables are seldom used. Syllables provide shorter sequences thancharacters, they can be extracted with rules, and their segmentation typicallyrequires less specialised effort than identifying morphemes. We reconsidersyllables for an open-vocabulary generation task in 20 languages. We userule-based syllabification methods for five languages and address the rest witha hyphenation tool, which behaviour as syllable proxy is validated. With acomparable perplexity, we show that syllables outperform characters, annotatedmorphemes and unsupervised subwords. Finally, we also study the overlapping ofsyllables concerning other subword pieces and discuss some limitations andopportunities.

Revisiting Neural Language Modelling with Syllables