Globally normalized neural sequence models are considered superior to their locally normalized equivalents because they may ameliorate the effects of label bias.
when considering high-capacity neural parametrizations that condition on the whole input sequence, both model classes are theoretically equivalent in terms of the distributions they are capable of representing.