We introduce an approach to train lexicalized parsers using bilingual corpora\nobtained by merging harmonized treebanks of different languages, producing\nparsers that can analyze sentences in either of the learned languages, or even\nsentences that mix both. We test the approach on the Universal Dependency\nTreebanks, training with MaltParser and MaltOptimizer. The results show that\nthese bilingual parsers are more than competitive, as most combinations not\nonly preserve accuracy, but some even achieve significant improvements over the\ncorresponding monolingual parsers. Preliminary experiments also show the\napproach to be promising on texts with code-switching and when more languages\nare added.\n