We develop Process Execution Graphs (PEG), a document-level representation of
real-world wet lab biochemistry protocols, addressing challenges such as
cross-sentence relations, long-range coreference, grounding, and implicit
arguments. We manually annotate PEGs in a corpus of complex lab protocols with
a novel interactive textual simulator that keeps track of entity traits and
semantic constraints during annotation. We use this data to develop
graph-prediction models, finding them to be good at entity identification and
local relation extraction, while our corpus facilitates further exploration of
challenging long-range relations.

本研究开发了进程执行图 (PEG)，这是实际湿实验生物化学方案的文档级表示，解决了交叉句子关系，长距离指代，接地和隐式论据等方面的挑战。我们使用数据开发了图形预测模型，发现它们在实体识别和本地关系提取方面表现良好，而我们的语料库促进了对具有挑战性的长距离关系的进一步探索。

使用交互式注释表示科学协议的过程级别表达形式

Process-Level Representation of Scientific Protocols with Interactive  Annotation

Representation learning is a critical ingredient for natural language
processing systems. Recent Transformer language models like BERT learn powerful
textual representations, but these models are targeted towards token- and
sentence-level training objectives and do not leverage information on
inter-document relatedness, which limits their document-level representation
power. For applications on scientific documents, such as classification and
recommendation, the embeddings power strong performance on end tasks. We
propose SPECTER, a new method to generate document-level embedding of
scientific documents based on pretraining a Transformer language model on a
powerful signal of document-level relatedness: the citation graph. Unlike
existing pretrained language models, SPECTER can be easily applied to
downstream applications without task-specific fine-tuning. Additionally, to
encourage further research on document-level models, we introduce SciDocs, a
new evaluation benchmark consisting of seven document-level tasks ranging from
citation prediction, to document classification and recommendation. We show
that SPECTER outperforms a variety of competitive baselines on the benchmark.

提出了一种新的方法 SPECTER 来基于引用图预训练 Transformer 语言模型以生成科学文件的文件级嵌入，在包括文献预测、文档分类和推荐等七种文档级任务上优于多种竞争基线。