  Scientific Information Extraction (ScientificIE) is a critical task that
involves the identification of scientific entities and their relationships. The
complexity of this task is compounded by the necessity for domain-specific
knowledge and the limited availability of annotated data. Two of the most
popular datasets for ScientificIE are SemEval-2018 Task-7 and SciERC. They have
overlapping samples and differ in their annotation schemes, which leads to
conflicts. In this study, we first introduced a novel approach based on
multi-task learning to address label variations. We then proposed a soft
labeling technique that converts inconsistent labels into probabilistic
distributions. The experimental results demonstrated that the proposed method
can enhance the model robustness to label noise and improve the end-to-end
performance in both ScientificIE tasks. The analysis revealed that label
variations can be particularly effective in handling ambiguous instances.
Furthermore, the richness of the information captured by label variations can
potentially reduce data size requirements. The findings highlight the
importance of releasing variation labels and promote future research on other
tasks in other domains. Overall, this study demonstrates the effectiveness of
multi-task learning and the potential of label variations to enhance the
performance of ScientificIE.
