# Exploiting Sentence Embedding for Medical Question Answering

Source: [Exploiting Sentence Embedding for Medical Question Answering](<https://www.sota2.com/research/paper/68fa65191e209131cea084c2>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T21:18:52.954Z

Authors: Yu Hao, Xien Liu, Ji Wu, Ping Lv

Published: 2018-11-15T03:38:20.000Z

## Abstract

Despite the great success of word embedding, sentence embedding remains a not-well-solved problem. In this paper, we present a supervised learning framework to exploit sentence embedding for the medical question answering task. The learning framework consists of two main parts: 1) a sentence embedding producing module, and 2) a scoring module. The former is developed with contextual self-attention and multi-scale techniques to encode a sentence into an embedding tensor. This module is shortly called Contextual self-Attention Multi-scale Sentence Embedding (CAMSE). The latter employs two scoring strategies: Semantic Matching Scoring (SMS) and Semantic Association Scoring (SAS). SMS measures similarity while SAS captures association between sentence pairs: a medical question concatenated with a candidate choice, and a piece of corresponding supportive evidence. The proposed framework is examined by two Medical Question Answering(MedicalQA) datasets which are collected from real-world applications: medical exam and clinical diagnosis based on electronic medical records (EMR). The comparison results show that our proposed framework achieved significant improvements compared to competitive baseline approaches. Additionally, a series of controlled experiments are also conducted to illustrate that the multi-scale strategy and the contextual self-attention layer play important roles for producing effective sentence embedding, and the two kinds of scoring strategies are highly complementary to each other for question answering problems.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/1811.06156>)

[PDF](<https://arxiv.org/pdf/1811.06156.pdf>)