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Recent advances in neural architectures, such as the Transformer, coupledwith the emergence of large-scale pre-trained models such as BERT, haverevolutionized the field of Natural Language Processing (NLP), pushing thestate of the art for a number of NLP tasks. A rich family of variations ofthese models has been proposed, such as RoBERTa, ALBERT, and XLNet, butfundamentally, they all remain limited in their ability to model certain kindsof information, and they cannot cope with certain information sources, whichwas easy for pre-existing models. Thus, here we aim to shed light on someimportant theoretical limitations of pre-trained BERT-style models that areinherent in the general Transformer architecture. First, we demonstrate inpractice on two general types of tasks -- segmentation and segment labeling --and on four datasets that these limitations are indeed harmful and thataddressing them, even in some very simple and naive ways, can yield sizableimprovements over vanilla RoBERTa and XLNet models. Then, we offer a moregeneral discussion on desiderata for future additions to the Transformerarchitecture that would increase its expressiveness, which we hope could helpin the design of the next generation of deep NLP architectures.

Transformers: "The End of History" for NLP?