In (attentional) encoder-decoder architectures for neural machine translation (Sutskever et al., 2014;Bahdanau et al., 2015), the decoder is essentially an RNN language model that is also conditioned on source context, so the first rationale, adding a language model to compensate for the independence assumptions of the translation model, does not apply.
the data argument is still valid in NMT, and we expect monolingual data to be especially helpful if parallel data is sparse, or a poor fit for the translation task, for instance because of a domain mismatch.