A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Authors: Honglun Zhang, Liqiang Xiao, Yongkun Wang, Yaohui Jin
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Extensive experiments on five benchmark datasets for text classification show that our model can significantly improve performances of related tasks with additional information from others. |
| Researcher Affiliation | Academia | Honglun Zhang1, Liqiang Xiao1, Yongkun Wang2, Yaohui Jin1,2 1State Key Lab of Advanced Optical Communication System and Network 2Network and Information Center Shanghai Jiao Tong University {ykw}@sjtu.edu.cn |
| Pseudocode | Yes | Algorithm 1 Task Oriented Sampling |
| Open Source Code | No | The paper does not provide any explicit statements about the release of source code or links to a code repository. |
| Open Datasets | Yes | As Table 1 shows, we select five benchmark datasets for text classification and design three experiment scenarios to evaluate the performances of our model. Multi-Cardinality Movie review datasets with different average lengths and class numbers, including SST1 [Socher et al., 2013], SST-2 and IMDB [Maas et al., 2011]. Multi-Domain Product review datasets on different domains from Multi-Domain Sentiment Dataset [Blitzer et al., 2007], including Books, DVDs, Electronics and Kitchen. Multi-Objective Classification datasets with different objectives, including IMDB, RN [Apt e et al., 1994] and QC [Li and Roth, 2002]. |
| Dataset Splits | Yes | We apply 10-fold cross-validation and different combinations of hyperparameters are investigated, of which the best one, as shown in Table 2, is reserved for comparisons with state-of-the-art models. |
| Hardware Specification | No | The paper does not explicitly describe the hardware used for running its experiments (e.g., specific GPU/CPU models). |
| Software Dependencies | No | The paper mentions Word2Vec but does not provide specific version numbers for any software dependencies. |
| Experiment Setup | Yes | Table 2: Hyperparameter settings Embedding size d = 300 Hidden layer size of LSTM n = 100 Initial learning rate η = 0.1 Regularization weight λ = 10-5 |