Neural Dynamic Focused Topic Model
Authors: Kostadin Cvejoski, Ramsés J. Sánchez, César Ojeda
AAAI 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our model on three different datasets (the UN general debates, the collection of NEURIPS papers, and the ACL Anthology dataset) and show that it (i) outperforms stateof-the-art topic models in generalization tasks and (ii) performs comparably to them on prediction tasks, while employing roughly the same number of parameters, and converging about two times faster. |
| Researcher Affiliation | Collaboration | Kostadin Cvejoski1, 2, Rams es J. S anchez1, 4, C esar Ojeda3 1 Lamarr-Institute for Machine Learning and Artificial Intelligence 2 Fraunhofer-Institute for Intelligent Analysis and Information Systems (IAIS) 3 University of Potsdam 4 BIT University of Bonn |
| Pseudocode | No | No pseudocode or clearly labeled algorithm blocks were found in the paper. |
| Open Source Code | Yes | Source code: https://github.com/cvejoski/Neural-Dynamic Focused-Topic-Model |
| Open Datasets | Yes | We evaluate our model on three datasets, namely the collection of UN speeches, NEURIPS papers and the ACL Anthology. The UN dataset3 (Baturo, Dasandi, and Mikhaylov 2017) (...) 3https://www.kaggle.com/unitednations/un-general-debates The NEURIPS dataset4 (...) 4https://www.kaggle.com/benhamner/nips-papers Finally, the ACL Anthology (Bird et al. 2008) |
| Dataset Splits | No | The paper mentions that |
| Hardware Specification | No | No specific hardware details (such as GPU models, CPU types, or cloud instance specifications) used for running experiments were provided in the paper. |
| Software Dependencies | No | No specific software dependencies with version numbers (e.g., Python 3.x, PyTorch 1.x) were explicitly stated in the paper. The paper mentions referring to supplementary material for setup details, but these are not provided in the main text. |
| Experiment Setup | Yes | Specifically, K was chosen from the set 50, 100 and 200. We found 50 to be the best value for all models, i.e. including the baselines. Similarly α0 was chosen from the set 0.1, 0.5, 1.0 |