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

End-to-end relation extraction (E2ERE) is an important task in information
extraction, more so for biomedicine as scientific literature continues to grow
exponentially. E2ERE typically involves identifying entities (or named entity
recognition (NER)) and associated relations, while most RE tasks simply assume
that the entities are provided upfront and end up performing relation
classification. E2ERE is inherently more difficult than RE alone given the
potential snowball effect of errors from NER leading to more errors in RE. A
complex dataset in biomedical E2ERE is the ChemProt dataset (BioCreative VI,
2017) that identifies relations between chemical compounds and genes/proteins
in scientific literature. ChemProt is included in all recent biomedical natural
language processing benchmarks including BLUE, BLURB, and BigBio. However, its
treatment in these benchmarks and in other separate efforts is typically not
end-to-end, with few exceptions. In this effort, we employ a span-based
pipeline approach to produce a new state-of-the-art E2ERE performance on the
ChemProt dataset, resulting in $> 4\%$ improvement in F1-score over the prior
best effort. Our results indicate that a straightforward fine-grained
tokenization scheme helps span-based approaches excel in E2ERE, especially with
regards to handling complex named entities. Our error analysis also identifies
a few key failure modes in E2ERE for ChemProt.

本研究通过使用基于跨度的管道方法，在 ChemProt 数据集上获得了新的最先进的 E2ERE（端到端关系提取）性能，结果比先前的最佳结果提高了 $> 4％$，结果表明简单的细粒度标记化方案有助于跨度方法在 E2ERE 中表现优异，特别是对于处理复杂命名实体。

化学蛋白相互作用提取的端到端模型：更好的标记和基于跨度的流水线策略

End-to-End Models for Chemical-Protein Interaction Extraction: Better  Tokenization and Span-Based Pipeline Strategies

Spoken Language Systems at Saarland University (LSV) participated this year
with 5 runs at the TAC KBP English slot filling track. Effective algorithms for
all parts of the pipeline, from document retrieval to relation prediction and
response post-processing, are bundled in a modular end-to-end relation
extraction system called RelationFactory. The main run solely focuses on
shallow techniques and achieved significant improvements over LSV's last year's
system, while using the same training data and patterns. Improvements mainly
have been obtained by a feature representation focusing on surface skip n-grams
and improved scoring for extracted distant supervision patterns. Important
factors for effective extraction are the training and tuning scheme for distant
supervision classifiers, and the query expansion by a translation model based
on Wikipedia links. In the TAC KBP 2013 English Slotfilling evaluation, the
submitted main run of the LSV RelationFactory system achieved the top-ranked
F1-score of 37.3%.

RelationFactory 是一个模块化的端到端关系抽取系统，通过表面跳过 n-gram 和改进的打分来提取关键信息并实现了 37.3% 的最高 F1 得分，利用维基百科链接的翻译模型扩展了查询。