Planting Trees in the Desert: Delexicalized Tagging and Parsing Combined

Various unsupervised and semi-supervised methods have been proposed to tag and parse an unseen language. We explore delexicalized parsing, proposed by (Zeman and Resnik, 2008), and delexicalized tagging, proposed by (Yu et al., 2016). For both approaches we provide a detailed evaluation on Universal Dependencies data (Nivre et al., 2016), a de-facto standard for multi-lingual morphosyntactic processing (while the previous work used other datasets). Our results confirm that in separation, each of the two delexicalized techniques has some limited potential when no annotation of the target language is available. However, if used in combination, their errors multiply beyond acceptable limits. We demonstrate that even the tiniest bit of expert annotation in the target language may contain significant potential and should be used if available.