Morphologically rich languages often lack the annotated linguistic resources
required to develop accurate natural language processing tools. We propose
models suitable for training morphological taggers with rich tagsets for
low-resource languages without using direct supervision. Our approach extends
existing approaches of projecting part-of-speech tags across languages, using
bitext to infer constraints on the possible tags for a given word type or
token. We propose a tagging model using Wsabie, a discriminative
embedding-based model with rank-based learning. In our evaluation on 11
languages, on average this model performs on par with a baseline
weakly-supervised HMM, while being more scalable. Multilingual experiments show
that the method performs best when projecting between related language pairs.
Despite the inherently lossy projection, we show that the morphological tags
predicted by our models improve the downstream performance of a parser by +0.6
LAS on average.

提出了一种适用于低资源语言的模型来训练形态标记器，该模型使用 Wesabie 模型进行打标， 通过在语料库中引入 POS 词性标注的元信息，将标记信息从富资源语言映射到贫资源语言，实现了跨语言知识的迁移，可以提高句法分析的效果。