(2016) prove that this class of locally normalized models that relies on the structural assumption of access to only left-to-right partial input at each step, is strictly less expressive than its globally normalized counterpart.
the standard sequence-to-sequence models used most often in practice and presented in this paper actually condition the decoder on a summary representation of the entire input sequence, x, computed by a neural encoder.