Exploiting Sentence Embedding for Medical Question Answering
Authors: Yu Hao, Xien Liu, Ji Wu, Ping Lv938-945
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 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. |
| Researcher Affiliation | Collaboration | 1Department of Electronic Engineering, Tsinghua University, Beijing, China haoy15@mails.tsinghua.edu.cn, {xeliu, wuji ee }@mail.tsinghua.edu.cn 2Tsinghua-i Flytek Joint Laboratory, i Flytek Research, Beijing, China luping ts@mail.tsinghua.edu.cn |
| Pseudocode | No | No pseudocode or algorithm blocks are provided; figures illustrate frameworks, not structured algorithms. |
| Open Source Code | No | The paper does not provide an unambiguous statement or link to open-source code for the described methodology. |
| Open Datasets | No | The datasets used (Medical QA#1 NMLEC and Medical QA#2 CD-EMR) are stated to be collected by the authors from specific sources (NMLEC exam, EMRs from hospitals) and there is no indication or link provided for their public availability. |
| Dataset Splits | No | The paper only explicitly mentions training and test sets for both Medical QA#1 and Medical QA#2 datasets (e.g., 'totally 250,000 medical questions as the training set' and 'the test set has 6,000 questions' for QA#1; 'The training set has 75265 items, and the test set has 16551 items' for QA#2), but no specific validation split or set is described. |
| Hardware Specification | No | The paper mentions training on 'the GPU' but does not specify any particular GPU model, CPU, or other hardware components (e.g., 'NVIDIA A100', 'Intel Xeon'). |
| Software Dependencies | No | The paper mentions 'Tensorflow (Abadi et al. 2016)' but does not provide a specific version number for Tensorflow or any other key software libraries. |
| Experiment Setup | Yes | The embedding s dimension is set to 200 for Medical QA#1 and 100 for Medical QA#2. [...] truncate all evidence documents and questions to no more than 100 words for Medical QA#1 and 70 words for Medical QA#2. For each candidate choice, only top 10 evidence documents are used to calculate the supportive score. The Bi-directional LSTM in the context layer has a dimension of 128. The size of attention encoding hidden state da (see Fig.3(b)) is 100. The number of semantics, r, is 15. Without any specification, in the multi-scale context layer of CAMSE framework, the size of convolution is 1,2,and 3. [...] We use Adam optimizer with exponential decay of learning rate and a dropout rate of 0.2 to reduce overfit, and the batch size is 10. |