A Generalized Language Model in Tensor Space
Authors: Lipeng Zhang, Peng Zhang, Xindian Ma, Shuqin Gu, Zhan Su, Dawei Song7450-7458
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on three sentence matching benchmark datasets demonstrate that DRr-Net has the ability to model sentence semantic more precisely and significantly improve the performance of sentence semantic matching. |
| Researcher Affiliation | Collaboration | Kun Zhang,1 Guangyi Lv,1 Linyuan Wang,1 Le Wu,2 Enhong Chen,1,* Fangzhao Wu,3 Xing Xie3 1Anhui Province Key Laboratory of Big Data Analysis and Application, School of Computer Science and Technology, University of Science and Technology of China {zhkun, gylv, wly757}@mail.ustc.edu.cn, cheneh@ustc.edu.cn 2Hefei University of Technology, China, lewu@hfut.edu.cn 3 Microsoft Research Asia, China, wufangzhao@gmail.com, xing.xie@microsoft.com |
| Pseudocode | No | The paper does not contain any pseudocode or algorithm blocks. |
| Open Source Code | No | During implementation, we utilize Photinia1 to build our entire model. 1https://github.com/Xoriie Inpottn/photinia. The link refers to a framework used, not the specific implementation code of DRr-Net. |
| Open Datasets | Yes | We evaluate our model on three well-studied datasets: the Stanford Natural Language Inference (SNLI), the Sentence Involving Compositional Knowledge (SICK), and Quora duplicate questions (Quora). |
| Dataset Splits | Yes | In order to get the best performance, we have tuned the hyper-parameters on the validation set. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory) used for running its experiments. |
| Software Dependencies | No | The paper mentions using 'Photinia' and 'Adam optimizer' but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | The dimension is set as 300. Character-level word embedding is set as 100. The number of stack layers in ASG unit is set as 3 and the re-read length in DRr unit is set as 6. The hidden state size of GRUs in these two units is set as 256. ... We use Adam optimizer with learning rate 10 4. |