Chinese Song Iambics Generation with Neural Attention-Based Model
Authors: Qixin Wang, Tianyi Luo, Dong Wang, Chao Xing
IJCAI 2016 | Conference PDF | Archive PDF | Plain Text | 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. |