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..
Chinese Song Iambics Generation with Neural Attention-Based Model
Authors: Qixin Wang, Tianyi Luo, Dong Wang, Chao Xing
IJCAI 2016 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Both the automatic and subjective evaluation results show that our model indeed can learn the complex structural and rhythmic patterns of Song iambics, and the generation is rather successful. |
| Researcher Affiliation | Collaboration | 1CSLT, RIIT, Tsinghua University, China 2Tsinghua National Lab for Information Science and Technology, Beijing, China 3Huilan Limited, Beijing, China 4CIST, Beijing University of Posts and Telecommunications, China |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper mentions using a 'word2vec tool1' with a URL (https://code.google.com/archive/p/word2vec/), but this refers to a third-party tool used for initialization, not the open-source code for the methodology described in the paper. |
| Open Datasets | No | The paper mentions using a 'Song iambics corpus (Songci)' collected from the Internet and the 'Gigaword corpus', but it does not provide concrete access information (link, DOI, repository, or formal citation with author/year for public access) for these datasets. |
| Dataset Splits | No | The paper specifies training and test sets ('15, 001 are used for training and 688 are used for test.') but does not mention a separate validation split. |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts) used for running the experiments are provided in the paper. |
| Software Dependencies | No | The paper mentions 'Moses tool [Koehn et al., 2007]' and 'Ada Delta algorithm [Zeiler, 2012]', but it does not provide specific version numbers for software components required to reproduce the experiment. |
| Experiment Setup | Yes | For the attention model, both the encoder and decoder involve a recurrent hidden layer that contains 500 hidden units, and a non-recurrent hidden layer that contains 600 units. A max-out non-linear layer is then employed to reduce the dimensionality to 300, followed by a linear transform to generate the output units that correspond to the possible Chinese characters. The model is trained with the Ada Delta algorithm [Zeiler, 2012], where the minibatch is set to be 60 sentences. |