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We introduce an approach to train lexicalized parsers using bilingual corporaobtained by merging harmonized treebanks of different languages, producingparsers that can analyze sentences in either of the learned languages, or evensentences that mix both. We test the approach on the Universal DependencyTreebanks, training with MaltParser and MaltOptimizer. The results show thatthese bilingual parsers are more than competitive, as most combinations notonly preserve accuracy, but some even achieve significant improvements over thecorresponding monolingual parsers. Preliminary experiments also show theapproach to be promising on texts with code-switching and when more languagesare added.

One model, two languages: training bilingual parsers with harmonized treebanks