Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols

Chaitanya Kulkarni, Jany Chan, Eric Fosler-Lussier, Raghu Machiraju


Abstract
Wet laboratory protocols (WLPs) are critical for conveying reproducible procedures in biological research. They are composed of instructions written in natural language describing the step-wise processing of materials by specific actions. This process flow description for reagents and materials synthesis in WLPs can be captured by material state transfer graphs (MSTGs), which encode global temporal and causal relationships between actions. Here, we propose methods to automatically generate a MSTG for a given protocol by extracting all action relationships across multiple sentences. We also note that previous corpora and methods focused primarily on local intra-sentence relationships between actions and entities and did not address two critical issues: (i) resolution of implicit arguments and (ii) establishing long-range dependencies across sentences. We propose a new model that incrementally learns latent structures and is better suited to resolving inter-sentence relations and implicit arguments. This model draws upon a new corpus WLP-MSTG which was created by extending annotations in the WLP corpora for inter-sentence relations and implicit arguments. Our model achieves an F1 score of 54.53% for temporal and causal relations in protocols from our corpus, which is a significant improvement over previous models - DyGIE++:28.17%; spERT:27.81%. We make our annotated WLP-MSTG corpus available to the research community.
Anthology ID:
2021.acl-long.525
Volume:
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)
Month:
August
Year:
2021
Address:
Online
Editors:
Chengqing Zong, Fei Xia, Wenjie Li, Roberto Navigli
Venues:
ACL | IJCNLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
6737–6750
Language:
URL:
https://aclanthology.org/2021.acl-long.525
DOI:
10.18653/v1/2021.acl-long.525
Bibkey:
Cite (ACL):
Chaitanya Kulkarni, Jany Chan, Eric Fosler-Lussier, and Raghu Machiraju. 2021. Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols. In Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers), pages 6737–6750, Online. Association for Computational Linguistics.
Cite (Informal):
Learning Latent Structures for Cross Action Phrase Relations in Wet Lab Protocols (Kulkarni et al., ACL-IJCNLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.acl-long.525.pdf
Optional supplementary material:
 2021.acl-long.525.OptionalSupplementaryMaterial.zip
Video:
 https://aclanthology.org/2021.acl-long.525.mp4
Data
WNUT 2020