David M. Schmidt ^* Philipp Cimiano

• Center of Cognitive Interaction Technology, Bielefeld University, Bielefeld, Germany

In the field of structured information extraction, there are typically semantic and syntactic constraints on the output of information extraction (IE) systems. These constraints, however, can typically not be guaranteed using standard (fine-tuned) encoder-decoder architectures. This has led to the development of constrained decoding approaches which allow, e.g., to specify constraints in form of context-free grammars. An open question is in how far an IE system can be effectively guided by a domain-specific grammar to ensure that the output structures follow the requirements of a certain domain data model. In this work we experimentally investigate the influence of grammar-constrained decoding as well as pointer generators on the performance of a domain-specific information extraction system. For this, we consider fine-tuned encoder-decoder models, Longformer and Flan-T5 in particular, and experimentally investigate whether the addition of grammar-constrained decoding and pointer generators improve information extraction results. Towards this goal, we consider the task of inducing structured representations from abstracts describing clinical trials, relying on the the C-TrO ontology to semantically describe the clinical trials and their results. We frame the task as a slot filling problem where certain slots of templates need to be filled with token sequences occurring in the input text. We use a dataset comprising of 211 annotated clinical trial abstracts about type 2 diabetes and glaucoma for training and evaluation. Our focus is on settings in which the available training data is in the order of a few hundred training examples, which we consider as a low resource setting. In all our experiments we could demonstrate the positive impact of grammar-constrained decoding, with an increase in F 1 score of pp 0.351 (absolute score 0.413) and pp 0.425 (absolute score 0.47) for the best-performing models on type 2 diabetes and glaucoma datasets, respectively. The addition of the pointer generators had a detrimental impact on the results, decreasing F 1 scores by pp 0.15 (absolute score 0.263) and pp 0.198 (absolute score 0.272) compared to the best-peforming pointer generator models on type 2 diabetes and glaucoma datasets, respectively.