The task builds on the CoNLL-2008 task and extends it to multiple languages. The core of the task is to predict syntactic and semantic dependencies and their labeling. Data is provided for both statistical training and evaluation, which extract these labeled dependencies from manually annotated treebanks such as the Penn Treebank for English, the Prague Dependency Treebank for Czech and similar treebanks for Catalan, Chinese, German, Japanese and Spanish languages, enriched with semantic relations (such as those captured in the Prop/Nombank and similar resources). Great effort has been devoted to provide the participants with a common and relatively simple data representation for all the languages, similar to the last year's English data.
Variants: CoNLL-2009
This dataset is used in 2 benchmarks:
| Task | Model | Paper | Date |
|---|---|---|---|
| Semantic Role Labeling | Ours (High-Order model) | End-to-end Semantic Role Labeling with … | 2021-01-02 |
| Dependency Parsing | CRFPar | Efficient Second-Order TreeCRF for Neural … | 2020-05-03 |
| Dependency Parsing | Biaffine Parser | Deep Biaffine Attention for Neural … | 2016-11-06 |
Recent papers with results on this dataset: