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Information Extraction (IE) from text refers to the task of extractingstructured knowledge from unstructured text. The task typically consists of aseries of sub-tasks such as Named Entity Recognition and Relation Extraction.Sourcing entity and relation type specific training data is a major bottleneckin domains with limited resources such as biomedicine. In this work we presenta slot filling approach to the task of biomedical IE, effectively replacing theneed for entity and relation-specific training data, allowing us to deal withzero-shot settings. We follow the recently proposed paradigm of coupling aTranformer-based bi-encoder, Dense Passage Retrieval, with a Transformer-basedreading comprehension model to extract relations from biomedical text. Weassemble a biomedical slot filling dataset for both retrieval and readingcomprehension and conduct a series of experiments demonstrating that ourapproach outperforms a number of simpler baselines. We also evaluate ourapproach end-to-end for standard as well as zero-shot settings. Our workprovides a fresh perspective on how to solve biomedical IE tasks, in theabsence of relevant training data. Our code, models and datasets are availableat https://github.com/ypapanik/biomedical-slot-filling.

Slot Filling for Biomedical Information Extraction