  We present data augmentation techniques for process extraction tasks in
scientific publications. We cast the process extraction task as a sequence
labeling task where we identify all the entities in a sentence and label them
according to their process-specific roles. The proposed method attempts to
create meaningful augmented sentences by utilizing (1) process-specific
information from the original sentence, (2) role label similarity, and (3)
sentence similarity. We demonstrate that the proposed methods substantially
improve the performance of the process extraction model trained on chemistry
domain datasets, up to 12.3 points improvement in performance accuracy
(F-score). The proposed methods could potentially reduce overfitting as well,
especially when training on small datasets or in a low-resource setting such as
in chemistry and other scientific domains.
