Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings

• URL: http://arxiv.org/abs/2401.07977v3
• Date: Fri, 13 Dec 2024 05:19:47 GMT
• Title: Towards Efficient Methods in Medical Question Answering using Knowledge Graph Embeddings
• Authors: Saptarshi Sengupta, Connor Heaton, Suhan Cui, Soumalya Sarkar, Prasenjit Mitra,
• Abstract summary: In Natural Language Processing (NLP), Machine Reading (MRC) is the task of answering a question based on a given context. To handle questions in the medical domain, modern language models such as BioBERT, SciBERT and even ChatGPT are trained on vast amounts of in-domain medical corpora. We propose a resource-efficient approach for injecting domain knowledge into a model without relying on such domain-specific pre-training.
• Score: 3.944219308229571
• License:
• Abstract: In Natural Language Processing (NLP), Machine Reading Comprehension (MRC) is the task of answering a question based on a given context. To handle questions in the medical domain, modern language models such as BioBERT, SciBERT and even ChatGPT are trained on vast amounts of in-domain medical corpora. However, in-domain pre-training is expensive in terms of time and resources. In this paper, we propose a resource-efficient approach for injecting domain knowledge into a model without relying on such domain-specific pre-training. Knowledge graphs are powerful resources for accessing medical information. Building on existing work, we introduce a method using Multi-Layer Perceptrons (MLPs) for aligning and integrating embeddings extracted from medical knowledge graphs with the embedding spaces of pre-trained language models (LMs). The aligned embeddings are fused with open-domain LMs BERT and RoBERTa that are fine-tuned for two MRC tasks, span detection (COVID-QA) and multiple-choice questions (PubMedQA). We compare our method to prior techniques that rely on a vocabulary overlap for embedding alignment and show how our method circumvents this requirement to deliver better performance. On both datasets, our method allows BERT/RoBERTa to either perform on par (occasionally exceeding) with stronger domain-specific models or show improvements in general over prior techniques. With the proposed approach, we signal an alternative method to in-domain pre-training to achieve domain proficiency. Our code is available here.

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