多树库解析求值的脆弱性

I. Alonso-Alonso, David Vilares, Carlos Gómez-Rodríguez
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引用次数: 0

摘要

分析评估的树库选择和可能由有偏差的选择产生的虚假效果尚未详细探讨。本文研究了对树库的单个子集的评价如何导致弱结论。首先,我们采用一些对比解析器,并在之前工作中提出的树库子集上运行它们,这些子集的使用在类型学或数据稀缺性等标准上是合理的(或不合理的)。其次,我们运行这个实验的大规模版本,创建大量的树库随机子集,并在它们上比较许多可用分数的解析器。结果显示,不同子集之间存在很大的差异,尽管建立良好的树库选择指南很困难,但一些不适当的策略可以很容易地避免。
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The Fragility of Multi-Treebank Parsing Evaluation
Treebank selection for parsing evaluation and the spurious effects that might arise from a biased choice have not been explored in detail. This paper studies how evaluating on a single subset of treebanks can lead to weak conclusions. First, we take a few contrasting parsers, and run them on subsets of treebanks proposed in previous work, whose use was justified (or not) on criteria such as typology or data scarcity. Second, we run a large-scale version of this experiment, create vast amounts of random subsets of treebanks, and compare on them many parsers whose scores are available. The results show substantial variability across subsets and that although establishing guidelines for good treebank selection is hard, some inadequate strategies can be easily avoided.
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