Evaluating the Robustness of Biomedical Concept Normalization

Biomedical concept normalization involves linking entity mentions in text to standard concepts in knowledge bases. It aids in resolving challenges to standardising ambiguous, variable terms in text or handling missing links. Therefore, it is one of the essential tasks of text mining that helps in effective information access and finds its utility in biomedical decision-making. Pre-trained language models (e.g., BERT) achieve impressive performance on this task. It has been observed that such models are insensitive to word order permutations and vulnerable to adversarial attacks on tasks like Text Classification, Natural Language Inference. However, the effect of such attacks is unknown for the task of Normalization, especially in the biomedical domain. In this paper, we propose heuristics-based Input Transformations (word level modifications and word order variations) and Adversarial Attacks to study the robustness of BERT-based normalization models across various datasets consisting of different biomedical entity types. We conduct experiments across three datasets: NCBI disease, BC5CDR Disease, and BC5CDR Chemical. We observe that for input transformations, pre-trained models often fail to detect invalid input. On the other hand, our proposed adversarial attacks that add imperceptible perturbations, result in affecting the ranking of a concept list for a given mention (or vice versa). We also generate natural adversarial examples that lead to performance degradation of $\sim$30\% in the F1-score. Additionally, we explore existing mitigation strategies to help a model recognize invalid inputs.