Context-guided Embedding Adaptation for Effective Topic Modeling in Low-Resource Regimes
Authors: Yishi Xu, Jianqiao Sun, Yudi Su, Xinyang Liu, Zhibin Duan, Bo Chen, Mingyuan Zhou
NeurIPS 2023 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We have conducted a wealth of quantitative and qualitative experiments, and the results show that our approach comprehensively outperforms established topic models. |
| Researcher Affiliation | Academia | Yishi Xu , Jianqiao Sun , Yudi Su, Xinyang Liu, Zhibin Duan, Bo Chen , National Key Laboratory of Radar Signal Processing, Xidian University, Xi an, China, 710071 {xuyishi, jianqiaosun}@stu.xidian.edu.cn, bchen@mail.xidian.edu.cn Mingyuan Zhou Mc Combs School of Business, The University of Texas at Austin, TX 78712, USA mingyuan.zhou@mccombs.utexas.edu |
| Pseudocode | Yes | In Alg. 1 and Alg. 2, we present the training and meta-testing procedures of our Meta-CETM. |
| Open Source Code | Yes | Our code is available at https://github.com/Novice Stone/Meta-CETM. |
| Open Datasets | Yes | We conducted experiments on four widely used textual benchmark datasets, specifically 20Newsgroups (20NG) [38], Yahoo Answers Topics (Yahoo) [39], DBpedia (DB14) [40], and Web of Science (WOS) [41]. |
| Dataset Splits | No | The paper describes a support set and a query set for each task (80%/20% split) but does not explicitly mention a separate validation set. |
| Hardware Specification | Yes | Finally, We train our model using the Adam optimizer [48] with a learning rate of 1 10 2 for 10 epochs on an NVIDIA Ge Force RTX 3090 graphics card. |
| Software Dependencies | No | The paper mentions 'spa Cy' and 'gensim package' but does not provide specific version numbers for these software dependencies. |
| Experiment Setup | Yes | For all compared methods, we set the number of topics as 10. And for all NTMs, the hidden layers size of the encoder is set to 300. For all embedding-based topic models, i.e., ETM, MAML-ETM, Meta-Saw ETM and our Meta-CETM, we load pretrained Glo Ve word embeddings [47] as the initialization for a fair comparison. Finally, We train our model using the Adam optimizer [48] with a learning rate of 1 10 2 for 10 epochs on an NVIDIA Ge Force RTX 3090 graphics card. |