  Recent advances in neural architectures, such as the Transformer, coupled
with the emergence of large-scale pre-trained models such as BERT, have
revolutionized the field of Natural Language Processing (NLP), pushing the
state of the art for a number of NLP tasks. A rich family of variations of
these models has been proposed, such as RoBERTa, ALBERT, and XLNet, but
fundamentally, they all remain limited in their ability to model certain kinds
of information, and they cannot cope with certain information sources, which
was easy for pre-existing models. Thus, here we aim to shed light on some
important theoretical limitations of pre-trained BERT-style models that are
inherent in the general Transformer architecture. First, we demonstrate in
practice on two general types of tasks -- segmentation and segment labeling --
and on four datasets that these limitations are indeed harmful and that
addressing them, even in some very simple and naive ways, can yield sizable
improvements over vanilla RoBERTa and XLNet models. Then, we offer a more
general discussion on desiderata for future additions to the Transformer
architecture that would increase its expressiveness, which we hope could help
in the design of the next generation of deep NLP architectures.
