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.
@article{arxiv.2101.10244,
title = {Process-Level Representation of Scientific Protocols with Interactive Annotation},
author = {Ronen Tamari and Fan Bai and Alan Ritter and Gabriel Stanovsky},
journal= {arXiv preprint arXiv:2101.10244},
year = {2021}
}
Comments
EACL 2021 camera ready. Data, models and code at https://textlabs.github.io/