MultiVec: a Multilingual and Multilevel Representation Learning Toolkit for NLP

We present MultiVec, a new toolkit for computing continuous representations for text at different granularity levels (word-level or sequences of words).MultiVec includes Mikolov et al. [2013b]'s word2vec features, Le and Mikolov [2014]'s paragraph vector (batch and online) and Luong et al. [2015]'s model for bilingual distributed representations.MultiVec also includes different distance measures between words and sequences of words.The toolkit is written in C++ and is aimed at being fast (in the same order of magnitude as word2vec), easy to use, and easy to extend.It has been evaluated on several NLP tasks: the analogical reasoning task, sentiment analysis, and crosslingual document classification.