MGAD: Multilingual Generation of Analogy Datasets
Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)
Abstract
We present a novel, minimally supervised method of generating word embedding evaluation datasets for a large number of languages. Our approach utilizes existing dependency treebanks and parsers in order to create language-specific syntactic analogy datasets that do not rely on translation or human annotation. As part of our work, we offer syntactic analogy datasets for three previously unexplored languages: Arabic, Hindi, and Russian. We further present an evaluation of three popular word embedding algorithms (Word2Vec,GloVe, LexVec) against these datasets and explore how the performance of each word embedding algorithm varies between several syntactic categories.