With a growing focus on morphological inflection systems for languages where
high-quality data is scarce, training data noise is a serious but so far
largely ignored concern. We aim at closing this gap by investigating the types
of noise encountered within a pipeline for truly unsupervised morphological
paradigm completion and its impact on morphological inflection systems: First,
we propose an error taxonomy and annotation pipeline for inflection training
data. Then, we compare the effect of different types of noise on multiple
state-of-the-art inflection models. Finally, we propose a novel character-level
masked language modeling (CMLM) pretraining objective and explore its impact on
the models' resistance to noise. Our experiments show that various
architectures are impacted differently by separate types of noise, but
encoder-decoders tend to be more robust to noise than models trained with a
copy bias. CMLM pretraining helps transformers, but has lower impact on LSTMs.

本文旨在探讨稀缺高质量数据的语言中的形态学屈折系统，包括对非监督形态学范式完成的管道内遇到的噪声类型进行了错误分类和注释流程、比较不同类型噪声对现有最新型变型模型的影响、再提出使用字符级屏蔽语言建模（CMLM）预训练目标探索其对模型的抗噪性的影响。实验发现，各种建筑物受到不同类型的噪声的影响不同，但编码器解码器比具有复制偏差的模型更为稳健。CMLM 预训练有助于变压器，但对 LSTM 影响较小。