SentiMedQAer: A Transfer Learning-Based Sentiment-Aware Model for Biomedical Question Answering

Xian Zhu, Yuanyuan Chen, Yueming Gu, Zhifeng Xiao

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Recent advances have witnessed a trending application of transfer learning in a broad spectrum of natural language processing (NLP) tasks, including question answering (QA). Transfer learning allows a model to inherit domain knowledge obtained from an existing model that has been sufficiently pre-trained. In the biomedical field, most QA datasets are limited by insufficient training examples and the presence of factoid questions. This study proposes a transfer learning-based sentiment-aware model, named SentiMedQAer, for biomedical QA. The proposed method consists of a learning pipeline that utilizes BioBERT to encode text tokens with contextual and domain-specific embeddings, fine-tunes Text-to-Text Transfer Transformer (T5), and RoBERTa models to integrate sentiment information into the model, and trains an XGBoost classifier to output a confidence score to determine the final answer to the question. We validate SentiMedQAer on PubMedQA, a biomedical QA dataset with reasoning-required yes/no questions. Results show that our method outperforms the SOTA by 15.83% and a single human annotator by 5.91%.

Original languageEnglish (US)
Article number773329
JournalFrontiers in Neurorobotics
Volume16
DOIs
StatePublished - Mar 10 2022

All Science Journal Classification (ASJC) codes

  • Biomedical Engineering
  • Artificial Intelligence

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