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 [1].
Sentence Generation for Entity Description with Content-Plan Attention
Authors: Bayu Trisedya, Jianzhong Qi, Rui Zhang9057-9064
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results show that our model outperforms stateof-the-art baselines by up to 3% and 5% in terms of BLEU score on two real-world datasets, respectively. |
| Researcher Affiliation | Academia | School of Computing and Information Systems, The University of Melbourne EMAIL, EMAIL |
| Pseudocode | No | The paper describes methods using mathematical equations and prose but does not include structured pseudocode or algorithm blocks. |
| Open Source Code | Yes | Code and dataset: http://www.ruizhang.info/GKB/gkb.htm |
| Open Datasets | Yes | We evaluate our model on two real-world datasets, including WIKIALL and WIKIBIO datasets. [...] The collected dataset contains 152, 231 triples of attributes, content-plan, and description (we call it the WIKIALL dataset). [...] For benchmarking, we also use the WIKIBIO dataset (Lebret, Grangier, and Auli 2016) which contains 728,321 biographies from Wikipedia. [...] Code and dataset: http://www.ruizhang.info/GKB/gkb.htm |
| Dataset Splits | Yes | We split each dataset into train set (80%), dev set (10%) and test set (10%). |
| Hardware Specification | No | No specific hardware details (e.g., GPU/CPU models, memory amounts, or detailed computer specifications) used for running experiments are provided in the paper. |
| Software Dependencies | No | No specific ancillary software details (e.g., library or solver names with version numbers) needed to replicate the experiment are provided. |
| Experiment Setup | No | The paper describes the model and training objectives but does not provide specific experimental setup details such as hyperparameter values (e.g., learning rate, batch size, number of epochs) or optimizer settings. |