Compressing Word Embeddings Using Syllables

Abstract

This work examines the possibility of using syllable embeddings, instead of the often used n-gram embeddings, as subword embeddings. We investigate this for two languages: English and Dutch. To this end, we also translated two standard English word embedding evaluation datasets, WordSim353 and SemEval-2017, to Dutch. Furthermore, we provide the research community with data sets of syllabic decompositions for both languages. We compare our approach to full word and n-gram embeddings. Compared to full word embeddings, we obtain English models that are 20 to 30 times smaller while retaining 80% of the performance. For Dutch, models are 15 times smaller for 70% performance retention. Although less accurate than the n-gram baseline we used, our models can be trained in a matter of minutes, as opposed to hours for the n-gram approach. We identify a path toward upgrading performance in future work. All code is made publicly available, as well as our collected English and Dutch syllabic decompositions and Dutch evaluation set translations.

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Reproductions