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..
A Generalized Recurrent Neural Architecture for Text Classification with Multi-Task Learning
Authors: Honglun Zhang, Liqiang Xiao, Yongkun Wang, Yaohui Jin
IJCAI 2017 | Venue PDF | 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 |