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..
Hype-HAN: Hyperbolic Hierarchical Attention Network for Semantic Embedding
Authors: Chengkun Zhang, Junbin Gao
IJCAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Hype-HAN is applied to large scale datasets. The empirical experiments show the effectiveness of our method. |
| Researcher Affiliation | Academia | Chengkun Zhang , Junbin Gao Discipline of Business Analytics, The University of Sydney Business School The University of Sydney, Camperdown, NSW 2006, Australia EMAIL |
| Pseudocode | No | The paper does not include pseudocode or clearly labeled algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described. |
| Open Datasets | Yes | We test Hype-HAN on some publicly available large-scale benchmark datasets with the same protocols from [Zhang et al., 2015; Yogatama et al., 2017]. The datasets include news classification (AGnews), question/answer categorization (Yahoo Answers), sentiment analysis (Yelp and Amazon) and Wikipedia article classification (DBpedia). |
| Dataset Splits | No | Table 1 provides 'Train' and 'Test' dataset sizes (e.g., AG. 120,000 for Train and 7,600 for Test), but no explicit validation split information is provided. |
| Hardware Specification | No | The experiment described in Section 4.2 states: "This experiment is conducted without a GPU". However, for the main experiments, no specific hardware details like CPU models, GPU models, or memory are provided. |
| Software Dependencies | No | The paper mentions initializing word embeddings via 'glove-50' and training with 'manifold-aware Riemannian ADAM', but it does not specify version numbers for these or any other software dependencies. |
| Experiment Setup | Yes | We initialize the word embedding via glove-50 [Pennington et al., 2014], and we set the word/sentence hidden state as 50dimension and train the models with manifold-aware Riemannian ADAM [B ecigneul and Ganea, 2018] with learning rate 0.001. We record the prediction accuracy on the test set around epoch 10-20 on smaller datasets (AGnews, DBpedia, Yelp), and around epoch 5-10 on the large-scale datasets (Yahoo and Amazon). |