Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Effective Neural Topic Modeling with Embedding Clustering Regularization
Authors: Xiaobao Wu, Xinshuai Dong, Thong Thanh Nguyen, Anh Tuan Luu
ICML 2023 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on benchmark datasets demonstrate that ECRTM effectively addresses the topic collapsing issue and consistently surpasses state-of-the-art baselines in terms of topic quality, topic distributions of documents, and downstream classification tasks. |
| Researcher Affiliation | Academia | Xiaobao Wu 1 Xinshuai Dong 2 Thong Nguyen 3 Anh Tuan Luu 1 1Nanyang Technological University 2Carnegie Mellon University 3National University of Singapore. |
| Pseudocode | Yes | Algorithm 1 Training algorithm for ECRTM. |
| Open Source Code | Yes | 1Our code is available at https://github.com/bobxwu/ECRTM |
| Open Datasets | Yes | Datasets We adopt the following benchmark document datasets for experiments: (i) 20 News Groups (20NG, Lang, 1995) is one of the most popular datasets for evaluating topic models, including news articles with 20 labels; (ii) IMDB (Maas et al., 2011) is the movie reviews containing two labels (positive and negative); (iii) Yahoo Answer (Zhang et al., 2015) is the question titles, contents, and the best answers from the Yahoo website with 10 labels, such as Society, Culture, and Family & Relationships; (iv) AG News (Zhang et al., 2015) contains news titles and descriptions, divided into 4 categories like Sports and Business. |
| Dataset Splits | No | The paper uses standard benchmark datasets, but it does not explicitly provide specific percentages, counts, or a detailed methodology for train/validation/test splits for its own experiments beyond general machine learning terminology. |
| Hardware Specification | No | No specific hardware details (e.g., exact GPU/CPU models, processor types, or memory amounts) were provided for running the experiments. |
| Software Dependencies | No | The paper mentions optimization algorithms like Adam and pre-trained embeddings like GloVe but does not provide specific software dependencies with version numbers (e.g., programming language versions, library versions like PyTorch or TensorFlow). |
| Experiment Setup | Yes | For the Sinkhorn s algorithm of ECRTM, we set the maximum number of iterations as 1,000, the stop tolerance 0.005, and ε 0.05 following Cuturi (2013). For our ECRTM, the prior distribution is specified with Laplace approximation (Hennig et al., 2012) to approximate a symmetric Dirichlet prior as µ0,k = 0 and Σ0,kk = (K 1)/(αK) with hyperparameter α. We set α as 1.0 following Card et al. (2018). Our encoder network is the same as Srivastava & Sutton (2017); Wu et al. (2020a;b): a MLP that has two linear layers with softplus activation function, concatenated with two single layers each for the mean and covariance matrix. We use Adam (Kingma & Ba, 2014) to optimize model parameters. |