Martins et al. (2008) presented what to the best of our knowledge still ranks as the best overall result on the CONLL-X Shared Task datasets. The paper shows how triads of stacked dependency parsers described in Martins et al. (2008) can label unlabeled data for each other in a way similar to co-training and produce end parsers that are significantly better than any of the stacked input parsers. We evaluate our system on five datasets from the CONLL-X Shared Task and obtain 10–20 % error reductions, incl. the best reported results on four of them. We compare our approach to other semisupervised learning algorithms. 1