Hype-HAN: Hyperbolic Hierarchical Attention Network for Semantic Embedding
Authors: Chengkun Zhang, Junbin Gao
IJCAI 2020 | Conference PDF | Archive PDF | Plain Text | 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 {chengkun.zhang, junbin.gao}@sydney.edu.au |
| 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). |