SegBot: A Generic Neural Text Segmentation Model with Pointer Network
Authors: Jing Li, Aixin Sun, Shafiq Joty
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | In our experiments, SEGBOT outperforms state-of-the-art models on both topic and EDU segmentation tasks. We conduct two sets of experiments to evaluate the effectiveness of SEGBOT: segmenting a document into topically coherent segments, and segmenting a sentence into EDUs. |
| Researcher Affiliation | Academia | Jing Li, Aixin Sun and Shafiq Joty School of Computer Science and Engineering, Nanyang Technological University, Singapore jli030@e.ntu.edu.sg, {axsun,srjoty}@ntu.edu.sg |
| Pseudocode | No | The paper describes the model with equations but does not include any structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper states: 'An online version of SEGBOT (EDU segmentation) is available at http://138.197.118.157:8000/segbot/.' This refers to a demo or online service, not the open-source code for the methodology itself. |
| Open Datasets | Yes | For evaluating topic segmentation models, we use the commonly used Choi dataset [Choi, 2000]. |
| Dataset Splits | Yes | We use the first 10% data of shuffled training set as development set for both Choi dataset and RST-DT dataset. |
| Hardware Specification | Yes | SEGBOT is implemented with Py Torch framework and evaluated on NVIDIA Tesla P100 GPU. |
| Software Dependencies | No | The paper states 'SEGBOT is implemented with Py Torch framework' but does not provide specific version numbers for PyTorch or any other software dependencies. |
| Experiment Setup | Yes | Table 1 gives the details of other hyper-parameter settings. We use Adam optimizer to update model parameters. In addition, we use gradient clipping by a max norm of 5 and l2 -regularization during training. |