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.

本文针对罕见疾病中涉及不连续和嵌套实体的复杂数据集，比较了三种主流的端到端关系抽取模型，并通过对错误分析来评估它们的性能。研究结果表明，管道模型仍然是最好的选择，而生成式预训练转换模型相较于管道模型和序列到序列模型表现较差。从当前情况来看，设计良好的管道模型在性能提升、成本和碳足迹方面具有显著优势。

基于管道、序列到序列和 GPT 模型的端到端关系抽取比较：用罕见疾病案例进行实验

Comparison of pipeline, sequence-to-sequence, and GPT models for  end-to-end relation extraction: experiments with the rare disease use-case

Named entity recognition (NER) remains challenging when entity mentions can
be discontinuous. Existing methods break the recognition process into several
sequential steps. In training, they predict conditioned on the golden
intermediate results, while at inference relying on the model output of the
previous steps, which introduces exposure bias. To solve this problem, we first
construct a segment graph for each sentence, in which each node denotes a
segment (a continuous entity on its own, or a part of discontinuous entities),
and an edge links two nodes that belong to the same entity. The nodes and edges
can be generated respectively in one stage with a grid tagging scheme and
learned jointly using a novel architecture named Mac. Then discontinuous NER
can be reformulated as a non-parametric process of discovering maximal cliques
in the graph and concatenating the spans in each clique. Experiments on three
benchmarks show that our method outperforms the state-of-the-art (SOTA)
results, with up to 3.5 percentage points improvement on F1, and achieves 5x
speedup over the SOTA model.

本文提出了一种基于 Mac 模型的分段图算法，用于解决实体识别中异构实体的问题。该算法可将异构实体视作图中的节点，通过识别图中的最大团并连接团内的连续实体段，实现对异构实体的非参数化处理，在三个基准数据集上的结果证明该方法领先于目前的最优算法，并且速度提高了 5 倍。