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.
@InProceedings{ABDOU18.1022, author = {Mostafa Abdou and Artur Kulmizev and Vinit Ravishankar}, title = "{MGAD: Multilingual Generation of Analogy Datasets}", booktitle = {Proceedings of the Eleventh International Conference on Language Resources and Evaluation (LREC 2018)}, year = {2018}, month = {May 7-12, 2018}, address = {Miyazaki, Japan}, editor = {Nicoletta Calzolari (Conference chair) and Khalid Choukri and Christopher Cieri and Thierry Declerck and Sara Goggi and Koiti Hasida and Hitoshi Isahara and Bente Maegaard and Joseph Mariani and Hélène Mazo and Asuncion Moreno and Jan Odijk and Stelios Piperidis and Takenobu Tokunaga}, publisher = {European Language Resources Association (ELRA)}, isbn = {979-10-95546-00-9}, language = {english} }
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