In this work, we advocate another approach and show that it is simpler and more effective to ignore unattached words and many-to-many alignments: we claim that training a parser from a corpus of highquality annotated (albeit partially) data will result in better parsing performances than a parser trained from fully-annotated but noisy data.
this solution comes at the expense of deleting words or creating fake dependencies in the target sentence, which may introduce unreliable annotations in the target data.