# Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning

arXiv: https://arxiv.org/abs/2011.13573 · HTML: https://mlsanity.com/paper/2011.13573/

## Key facts

- Cited by 6 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 27 Nov 2020.

## Built on by (most cited first, 6 of 6)

- [ERNIE 3.0: Large-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2107.02137/) (2021)
- [KAG: Boosting LLMs in Professional Domains via Knowledge Augmented Generation](https://mlsanity.com/paper/2409.13731/) (2024)
- [ERNIE 3.0 Titan: Exploring Larger-scale Knowledge Enhanced Pre-training for Language Understanding and Generation](https://mlsanity.com/paper/2112.12731/) (2021)
- [Efficient Knowledge Infusion via KG-LLM Alignment](https://mlsanity.com/paper/2406.03746/) (2024)
- [SkillNet-NLU: A Sparsely Activated Model for General-Purpose Natural Language Understanding](https://mlsanity.com/paper/2203.03312/) (2022)
- [ChiMed 2.0: Advancing Chinese Medical Dataset in Facilitating Large Language Modeling](https://mlsanity.com/paper/2507.15275/) (2025)

## Abstract (arXiv)

Chinese medical question-answer matching is more challenging than the open-domain question answer matching in English. Even though the deep learning method has performed well in improving the performance of question answer matching, these methods only focus on the semantic information inside sentences, while ignoring the semantic association between questions and answers, thus resulting in performance deficits. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. To better adapt to Chinese medical question-answer matching and take the advantages of different neural network structures, we propose the Crossed BERT network to extract the deep semantic information inside the sentence and the semantic association between question and answer, and then combine with the multi-scale CNNs network or BiGRU network to take the advantage of different structure of neural networks to learn more semantic features into the sentence representation. The experiments on the cMedQA V2.0 and cMedQA V1.0 dataset show that our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching.