Meta-Complementing the Semantics of Short Texts in Neural Topic Models
Authors: Delvin Ce Zhang, Hady Lauw
NeurIPS 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments demonstrate the advantage of our framework. |
| Researcher Affiliation | Academia | Delvin Ce Zhang School of Computing and Information Systems Singapore Management University Singapore 178902 cezhang.2018@smu.edu.sg Hady W. Lauw School of Computing and Information Systems Singapore Management University Singapore 178902 hadywlauw@smu.edu.sg |
| Pseudocode | No | The paper mentions a learning algorithm in supplementary materials but does not include pseudocode or an algorithm block in the main text. |
| Open Source Code | Yes | We include the source code, datasets, and a readme file for reproducibility. |
| Open Datasets | Yes | Cora [24] is corpus of academic papers with citations as links. ... In addition, HEP-TH [21] is a corpus of Physics papers with their citations. Web [20] is a Web page hyperlink network where each page is a news article... |
| Dataset Splits | Yes | We split 80% documents for training (10% are for validation). |
| Hardware Specification | Yes | All the experiments were done on Linux server with a Tesla K80 GPU with 11441Mi B. |
| Software Dependencies | No | The paper does not provide specific version numbers for software dependencies beyond mentioning general tools like 'Glove embeddings'. |
| Experiment Setup | Yes | We set L = 2 convolutional layers. λgen = 2 and λreg = 0.05. Number of negative samples M = 5 and number of semantic clusters R = 5. L is the median length of the corpus. Local and global learning rates are α1 = 0.001 and α2 = 0.0005. We use 300D Glove embeddings. |