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.

本文研究使用音节嵌入代替常用的n-gram嵌入作为子词嵌入的可行性，并在英语和荷兰语中进行了探讨。与完整单词嵌入相比，我们的模型英语表现保留80％，大小为原来的20至30倍，荷兰语表现保留70％，大小为原来的15倍，并且能够在短时间内进行训练，但比n-gram基线方法略逊。

利用音节压缩词向量