@article{TACL897,
	author = {Gorman, Kyle  and Sproat, Richard },
	title = {Minimally Supervised Number Normalization},
	journal = {Transactions of the Association for Computational Linguistics},
	volume = {4},
	year = {2016},
	keywords = {},
	abstract = {We propose two models for verbalizing numbers, a key component in speech recognition and synthesis systems. The first model uses an end-to-end recurrent neural network. The second model, drawing inspiration from the linguistics literature, uses finite-state transducers constructed with a minimal amount of training data. While both models achieve near-perfect performance, the latter model can be trained using several orders of magnitude less data than the former, making it particularly useful for low-resource languages.},
	issn = {2307-387X},
	url = {https://www.transacl.org/ojs/index.php/tacl/article/view/897},
	pages = {507--519}
}