Neural Generative Question Answering
Authors: Jun Yin, Xin Jiang, Zhengdong Lu, Lifeng Shang, Hang Li, Xiaoming Li
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical study shows the proposed model can effectively deal with the variations of questions and answers, and generate right and natural answers by referring to the facts in the knowledge-base. The experiment on question answering demonstrates that the proposed model can outperform an embedding-based QA model as well as a neural dialogue model trained on the same data. |
| Researcher Affiliation | Collaboration | 1School of Electronic Engineering and Computer Science, Peking University 2Noah s Ark Lab, Huawei Technologies 3Collaborative Innovation Center of High Performance Computing, NUDT, Changsha, China |
| Pseudocode | No | The paper describes the model architecture and training process in text and mathematical formulas, but it does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | The data is publicly available online3. 3https://github.com/jxfeb/Generative QA |
| Open Datasets | Yes | To facilitate research on the task of generative QA, we create a new dataset by collecting data from the web. ... The data is publicly available online3. 3https://github.com/jxfeb/Generative QA |
| Dataset Splits | No | The data is further randomly partitioned into training dataset and test dataset by using triple as the partition key. ... Table 3 shows some statistics of the datasets. |
| Hardware Specification | Yes | Our models are trained on an NVIDIA Tesla K40 GPU using Theano [Bastien et al., 2012; Bergstra et al., 2010], with the mini-batch size of 80. |
| Software Dependencies | No | The paper mentions using "Jieba Chinese word segmentor" and "Theano" but does not provide specific version numbers for these software components. |
| Experiment Setup | Yes | The dimensions of the hidden states of encoder and decoder are both set to 500, and the dimension of the word-embedding is set to 300. Our models are trained on an NVIDIA Tesla K40 GPU using Theano [Bastien et al., 2012; Bergstra et al., 2010], with the mini-batch size of 80. The training of each model takes about two or three days. |