# Exploiting Sentence Embedding for Medical Question Answering

arXiv: https://arxiv.org/abs/1811.06156 · HTML: https://mlsanity.com/paper/1811.06156/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Nov 2018.

## Built on by (most cited first, 5 of 5)

- [Evaluating Unsupervised Dimensionality Reduction Methods for Pretrained Sentence Embeddings](https://mlsanity.com/paper/2403.14001/) (2024)
- [Compressing Sentence Representation for Semantic Retrieval via Homomorphic Projective Distillation](https://mlsanity.com/paper/2203.07687/) (2022)
- [UniMoCo: Unified Modality Completion for Robust Multi-Modal Embeddings](https://mlsanity.com/paper/2505.11815/) (2025)
- [Robust and Minimally Invasive Watermarking for EaaS](https://mlsanity.com/paper/2410.17552/) (2024)
- [Medical Question Understanding and Answering with Knowledge Grounding and Semantic Self-Supervision](https://mlsanity.com/paper/2209.15301/) (2022)

## Abstract (arXiv)

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.