  The utility of linguistic annotation in neural machine translation seemed to
had been established in past papers. The experiments were however limited to
recurrent sequence-to-sequence architectures and relatively small data
settings. We focus on the state-of-the-art Transformer model and use comparably
larger corpora. Specifically, we try to promote the knowledge of source-side
syntax using multi-task learning either through simple data manipulation
techniques or through a dedicated model component. In particular, we train one
of Transformer attention heads to produce source-side dependency tree. Overall,
our results cast some doubt on the utility of multi-task setups with linguistic
information. The data manipulation techniques, recommended in previous works,
prove ineffective in large data settings. The treatment of self-attention as
dependencies seems much more promising: it helps in translation and reveals
that Transformer model can very easily grasp the syntactic structure. An
important but curious result is, however, that identical gains are obtained by
using trivial "linear trees" instead of true dependencies. The reason for the
gain thus may not be coming from the added linguistic knowledge but from some
simpler regularizing effect we induced on self-attention matrices.
