Fusing Char-word to Enhance Semantics with Multi-attentive for Medical Question Answering
LYU Yuke
LYU Hongqing
ZHOU Yanping
Abstract：Chinese medical question answer are made more challenging by their language and domain specificity.To better represent the Sentence meaning of medical question answer,this paper proposes a method for fusing char-word to enhance semantics with multi-attentive for medical question answering.Firstly,the pre-trained models BERT and WordBERT are used to extract the vector representations of the text at the word level and character level respectively,and the two are fused to obtain more complete se-mantic information of the sentence vector.Then,an attention mechanism is added to generate an answer representation containing information about the question,which is input to bidirectional gating recurrent unit to obtain the overall semantic features of the sen-tence.Finally,the multi-attention pooling module enables the interaction of relevance between questions and answers,and finds the best matching answer by calculating the similarity of question-answer pairs.Experimental notes on the cMedQA medical dataset,the method in this paper has an improved on ACC@1 compared to other deep learning-based methods,it is demonstrated that fusing vector representations of char-words with the addition of attention in front of the neural network can improve the performance of auto-matic question answer models.
Keywords：question answeringfusing char-wordattentionbi-directional gated recurrent unitsimilarity matching
Publication Date：2025-11-20
Online Publishing Date：2026-01-28(First online date of this platform, not the publication date of the document)
Pages：6( 3109-3114 )