Regularization Advantages of Multilingual Neural Language Models for Low Resource Domains
Table 1: Number of words in the splits obtained from the Babel collection Gales et al. (2014).
['[EMPTY]', 'Creole', 'Swahili', 'Tagalog', 'Turkish']
For evaluation we use the conversational data of four low-resource languages, namely Creole, Swahili, Tagalog, and Turkish, taken from language packs released within IARPA Babel program Gales et al. Every language pack contains a training and a development set, containing text of audio transcripts. We use the development set for testing and split the given training set into training and validation sets, where the size of the validation set is the same or close to the testing set. We apply punctuation removal and set the texts to lower case. Similar to Ragni et al.