Abstract
The usefulness of part-of-speech tags for parsing has been heavily questioned due to the success of word-contextualized parsers. Yet, most studies are limited to coarse-grained tags and high quality written content; while we know little about their influence when it comes to models in production that face lexical errors. We expand these setups and design an adversarial attack to verify if the use of morphological information by parsers: (i) contributes to error propagation or (ii) if on the other hand it can play a role to correct mistakes that word-only neural parsers make. The results on 14 diverse UD treebanks show that under such attacks, for transition- and graph-based models their use contributes to degrade the performance even faster, while for the (lower-performing) sequence labeling parsers they are helpful. We also show that if morphological tags were utopically robust against lexical perturbations, they would be able to correct parsing mistakes.
Abstract (translated)
词干标记对于解析的有用性一直受到严重质疑，因为这得益于 word-contextualization 解析器的成功。然而，大多数研究都局限于粒度粗的标记和高质量的写作内容；而我们对于面临词汇错误 production 模型的影响了解得很少。我们扩展这些 setups 并设计了一场对抗攻击来验证parsers 使用形态信息的作用：(i)是否会促进错误传播，(ii)如果他们能够发挥作用来纠正仅使用单词神经网络解析器犯下的错误。对14个不同的 UD 树库的结果表明，在这些攻击下，对于过渡和图形模型，他们的使用加速了性能的下降，而对于低表现序列标签解析器则它们是有益的。我们还表明，如果形态标记乌托邦地 robust 于词汇颠覆，它们能够纠正解析错误。
URL
https://arxiv.org/abs/2305.15119
