Data-driven segmentation of words into subword units has been used in various
natural language processing applications such as automatic speech recognition
and statistical machine translation for almost 20 years. Recently it has became
more widely adopted, as models based on deep neural networks often benefit from
subword units even for morphologically simpler languages. In this paper, we
discuss and compare training algorithms for a unigram subword model, based on
the Expectation Maximization algorithm and lexicon pruning. Using English,
Finnish, North Sami, and Turkish data sets, we show that this approach is able
to find better solutions to the optimization problem defined by the Morfessor
Baseline model than its original recursive training algorithm. The improved
optimization also leads to higher morphological segmentation accuracy when
compared to a linguistic gold standard. We publish implementations of the new
algorithms in the widely-used Morfessor software package.

本文介绍了一种针对单语词根基于期望最大化算法和词典剪枝的 unigram 子词模型的训练算法，并比较了不同算法在英语、芬兰语、北萨米语和土耳其语数据集上的效果。通过改进优化问题，提高了基于语言黄金标准的单词分割的准确率。同时，我们在广泛使用的 Morfessor 软件包中公开了新算法的实现。