We evaluate the efficacy of predicted UPOS tags as input features for dependency parsers in lower resource settings to evaluate how treebank size affects the impact tagging accuracy has on parsing performance. We do this for real low resource universal dependency treebanks, artificially low resource data with varying treebank sizes, and for very small treebanks with varying amounts of augmented data. We find that predicted UPOS tags are somewhat helpful for low resource treebanks, especially when fewer fully-annotated trees are available. We also find that this positive impact diminishes as the amount of data increases.
翻译:我们评估了预测的UPOS标签作为低资源环境中受抚养人分析器输入功能的功效,以评价树银行规模如何影响标记精确度对分析性能的影响。我们这样做是为了真正的低资源普遍依赖树银行、树银行规模不同的人为低资源数据,以及数量不同的扩大数据规模极小的树银行。我们发现,预测的UPOS标签对低资源树银行有些帮助,特别是当可获得充分注释的树木较少的时候。我们还发现,随着数据数量的增加,这种积极影响会减少。