Morphological Segmentation involves decomposing words into morphemes, the smallest meaning-bearing units of language. This is an important NLP task for morphologically-rich agglutinative languages such as the Southern African Nguni language group. In this paper, we investigate supervised and unsupervised models for two variants of morphological segmentation: canonical and surface segmentation. We train sequence-to-sequence models for canonical segmentation, where the underlying morphemes may not be equal to the surface form of the word, and Conditional Random Fields (CRF) for surface segmentation. Transformers outperform LSTMs with attention on canonical segmentation, obtaining an average F1 score of 72.5% across 4 languages. Feature-based CRFs outperform bidirectional LSTM-CRFs to obtain an average of 97.1% F1 on surface segmentation. In the unsupervised setting, an entropy-based approach using a character-level LSTM language model fails to outperforms a Morfessor baseline, while on some of the languages neither approach performs much better than a random baseline. We hope that the high performance of the supervised segmentation models will help to facilitate the development of better NLP tools for Nguni languages.

本文研究了两种形态学分割方法的监督和无监督模型，并使用序列到序列模型和条件随机场对其进行训练，利用变压器技术在规范化分割上优于带有注意力的LSTM，并使用基于特征的CRF优于双向LSTM-CRF，在有监督的情况下获得97.1％的F1分数，但在无监督的情况下，该模型的效果并不理想，因此我们希望高性能的监督分割模型将有助于促进发展更好的NLP工具。

Nguni语系的规范和表层形态分割