Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning

• URL: http://arxiv.org/abs/2011.13573v1
• Date: Fri, 27 Nov 2020 06:13:56 GMT
• Title: Chinese Medical Question Answer Matching Based on Interactive Sentence Representation Learning
• Authors: Xiongtao Cui and Jungang Han
• Abstract summary: Chinese medical question-answer matching is more challenging than the open-domain question answer matching in English. In this paper, we design a series of interactive sentence representation learning models to tackle this problem. Our model significantly outperforms all the existing state-of-the-art models of Chinese medical question answer matching.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

Related papers

• Medical Vision-Language Pre-Training for Brain Abnormalities [96.1408455065347]
  We show how to automatically collect medical image-text aligned data for pretraining from public resources such as PubMed. In particular, we present a pipeline that streamlines the pre-training process by initially collecting a large brain image-text dataset. We also investigate the unique challenge of mapping subfigures to subcaptions in the medical domain.
  arXiv  Detail & Related papers  (2024-04-27T05:03:42Z)
• Explanatory Argument Extraction of Correct Answers in Resident Medical Exams [5.399800035598185]
  We present a new dataset which includes not only explanatory arguments for the correct answer, but also arguments to reason why the incorrect answers are not correct. This new benchmark allows us to setup a novel extractive task which consists of identifying the explanation of the correct answer written by medical doctors.
  arXiv  Detail & Related papers  (2023-12-01T13:22:35Z)
• ViMQ: A Vietnamese Medical Question Dataset for Healthcare Dialogue System Development [1.4315915057750197]
  We publish a Vietnamese dataset of medical questions from patients with sentence-level and entity-level annotations. We propose a simple self-supervised training strategy with span-noise modelling that improves the performance.
  arXiv  Detail & Related papers  (2023-04-27T17:59:53Z)
• A Dual-Attention Learning Network with Word and Sentence Embedding for Medical Visual Question Answering [2.0559497209595823]
  Research in medical visual question answering (MVQA) can contribute to the development of computer-aided diagnosis. Existing MVQA question extraction schemes mainly focus on word information, ignoring medical information in the text. In this study, a dual-attention learning network with word and sentence embedding (WSDAN) is proposed.
  arXiv  Detail & Related papers  (2022-10-01T08:32:40Z)
• CBLUE: A Chinese Biomedical Language Understanding Evaluation Benchmark [51.38557174322772]
  We present the first Chinese Biomedical Language Understanding Evaluation benchmark. It is a collection of natural language understanding tasks including named entity recognition, information extraction, clinical diagnosis normalization, single-sentence/sentence-pair classification. We report empirical results with the current 11 pre-trained Chinese models, and experimental results show that state-of-the-art neural models perform by far worse than the human ceiling.
  arXiv  Detail & Related papers  (2021-06-15T12:25:30Z)
• A Multi-Size Neural Network with Attention Mechanism for Answer Selection [3.310455595316906]
  An effective architecture,multi-size neural network with attention mechanism (AM-MSNN),is introduced into the answer selection task. It captures more levels of language granularities in parallel, because of the various sizes of filters comparing with single-layer CNN and multi-layer CNNs. It extends the sentence representations by attention mechanism, thus containing more information for different types of questions.
  arXiv  Detail & Related papers  (2021-04-24T02:13:26Z)
• Where's the Question? A Multi-channel Deep Convolutional Neural Network for Question Identification in Textual Data [83.89578557287658]
  We propose a novel multi-channel deep convolutional neural network architecture, namely Quest-CNN, for the purpose of separating real questions. We conducted a comprehensive performance comparison analysis of the proposed network against other deep neural networks. The proposed Quest-CNN achieved the best F1 score both on a dataset of data entry-review dialogue in a dialysis care setting, and on a general domain dataset.
  arXiv  Detail & Related papers  (2020-10-15T15:11:22Z)
• Interpretable Multi-Step Reasoning with Knowledge Extraction on Complex Healthcare Question Answering [89.76059961309453]
  HeadQA dataset contains multiple-choice questions authorized for the public healthcare specialization exam. These questions are the most challenging for current QA systems. We present a Multi-step reasoning with Knowledge extraction framework (MurKe) We are striving to make full use of off-the-shelf pre-trained models.
  arXiv  Detail & Related papers  (2020-08-06T02:47:46Z)
• Knowledgeable Dialogue Reading Comprehension on Key Turns [84.1784903043884]
  Multi-choice machine reading comprehension (MRC) requires models to choose the correct answer from candidate options given a passage and a question. Our research focuses dialogue-based MRC, where the passages are multi-turn dialogues. It suffers from two challenges, the answer selection decision is made without support of latently helpful commonsense, and the multi-turn context may hide considerable irrelevant information.
  arXiv  Detail & Related papers  (2020-04-29T07:04:43Z)
• Information-Theoretic Probing for Linguistic Structure [74.04862204427944]
  We propose an information-theoretic operationalization of probing as estimating mutual information. We evaluate on a set of ten typologically diverse languages often underrepresented in NLP research.
  arXiv  Detail & Related papers  (2020-04-07T01:06:36Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.