论文标题
跨语性变形作为解析的数据增强方法
Cross-lingual Inflection as a Data Augmentation Method for Parsing
论文作者
论文摘要
我们提出了一种基于形态的低资源（LR）依赖性解析方法。我们训练目标LR语言的形态膨胀器，并将其应用于相关的富库（RR）Treebanks，以创建类似于目标LR语言的跨语言（X型）Treebanks。我们使用这种弯曲的树库来培训零（X型树库培训）和很少的射击（X型和目标语言Treebanks）设置的解析器。结果表明，该方法有时会改善基准，但并非一致。
We propose a morphology-based method for low-resource (LR) dependency parsing. We train a morphological inflector for target LR languages, and apply it to related rich-resource (RR) treebanks to create cross-lingual (x-inflected) treebanks that resemble the target LR language. We use such inflected treebanks to train parsers in zero- (training on x-inflected treebanks) and few-shot (training on x-inflected and target language treebanks) setups. The results show that the method sometimes improves the baselines, but not consistently.
