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Scaling existing applications and solutions to multiple human languages hastraditionally proven to be difficult, mainly due to the language-dependentnature of preprocessing and feature engineering techniques employed intraditional approaches. In this work, we empirically investigate the factorsaffecting language-independent models built with multilingual representations,including task type, language set and data resource. On two most representativeNLP tasks -- sentence classification and sequence labeling, we show thatlanguage-independent models can be comparable to or even outperforms the modelstrained using monolingual data, and they are generally more effective onsentence classification. We experiment language-independent models with manydifferent languages and show that they are more suitable for typologicallysimilar languages. We also explore the effects of different data sizes whentraining and testing language-independent models, and demonstrate that they arenot only suitable for high-resource languages, but also very effective inlow-resource languages.

An Empirical Study of Factors Affecting Language-Independent Models