Cross-lingual RST Discourse Parsing
Table 1: Number of documents (#Doc), trees (#Trees, less than #Doc when we were unable to parse a document, see Section 4.2), words (#Words, see Section 6), relations (#Rel, originally), labels (#Lab, relation and nuclearity), EDUs (#EDU, max/min/avg number of EDUs per document), and CDUs (#CDU).
['Corpus', '#Doc', '#Trees', '#Words', '#Rel', '#Lab', '#EDU', 'max/min/avg', '#CDU']
For the other languages, performance are still high for unlabeled structure, but far lower for labeled structure except for Spanish. For this language, the quite high performance obtained were unexpected, since the corpus is far much smaller than the Portuguese one. One possible explanation is that the Portuguese corpus is in fact a mix of different corpora, with varied domains, and possibly changes in annotation choices.