  End-to-end relation extraction (E2ERE) is an important and realistic
application of natural language processing (NLP) in biomedicine. In this paper,
we aim to compare three prevailing paradigms for E2ERE using a complex dataset
focused on rare diseases involving discontinuous and nested entities. We use
the RareDis information extraction dataset to evaluate three competing
approaches (for E2ERE): NER $\rightarrow$ RE pipelines, joint sequence to
sequence models, and generative pre-trained transformer (GPT) models. We use
comparable state-of-the-art models and best practices for each of these
approaches and conduct error analyses to assess their failure modes. Our
findings reveal that pipeline models are still the best, while
sequence-to-sequence models are not far behind; GPT models with eight times as
many parameters are worse than even sequence-to-sequence models and lose to
pipeline models by over 10 F1 points. Partial matches and discontinuous
entities caused many NER errors contributing to lower overall E2E performances.
We also verify these findings on a second E2ERE dataset for chemical-protein
interactions. Although generative LM-based methods are more suitable for
zero-shot settings, when training data is available, our results show that it
is better to work with more conventional models trained and tailored for E2ERE.
More innovative methods are needed to marry the best of the both worlds from
smaller encoder-decoder pipeline models and the larger GPT models to improve
E2ERE. As of now, we see that well designed pipeline models offer substantial
performance gains at a lower cost and carbon footprint for E2ERE. Our
contribution is also the first to conduct E2ERE for the RareDis dataset.
