  The use of linguistic typological resources in natural language processing
has been steadily gaining more popularity. It has been observed that the use of
typological information, often combined with distributed language
representations, leads to significantly more powerful models. While linguistic
typology representations from various resources have mostly been used for
conditioning the models, there has been relatively little attention on
predicting features from these resources from the input data. In this paper we
investigate whether the various linguistic features from World Atlas of
Language Structures (WALS) can be reliably inferred from multi-lingual text.
Such a predictor can be used to infer structural features for a language never
observed in training data. We frame this task as a multi-label classification
involving predicting the set of non-mutually exclusive and extremely sparse
multi-valued labels (WALS features). We construct a recurrent neural network
predictor based on byte embeddings and convolutional layers and test its
performance on 556 languages, providing analysis for various linguistic types,
macro-areas, language families and individual features. We show that some
features from various linguistic types can be predicted reliably.
