This paper describes the LIGM-Alpage system for the SPMRL 2013 Shared Task. We only participated to the French part of the dependency parsing track, focusing on the realistic setting where the system is informed neither with gold tagging and morphology nor (more importantly) with gold grouping of tokens into multi-word expressions (MWEs). While the realistic scenario of predicting both MWEs and syntax has already been investigated for constituency parsing, the SPMRL 2013 shared task datasets offer the possibility to investigate it in the dependency framework. We obtain the best results for French, both for overall parsing and for MWE recognition, using a reparsing architecture that combines several parsers, with both pipeline architecture (MWE recognition followed by parsing), and joint architecture (MWE recognition performed by the parser).