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 Neural Model for Joint Event Detection and Summarization
Authors: Zhongqing Wang, Yue Zhang
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments show that our proposed neural joint model is more effective compared to its pipeline baseline. |
| Researcher Affiliation | Academia | Zhongqing Wang , and Yue Zhang Soochow University, Suzhou, China Singapore University of Technology and Design, Singapore |
| Pseudocode | No | The paper describes algorithmic steps and models using mathematical formulations and descriptive text (e.g., in Section 3.2), but it does not include explicitly labeled pseudocode or algorithm blocks. |
| Open Source Code | Yes | We release the code and data sets of this paper at https://github. com/wangzq870305/joint_event_detection. |
| Open Datasets | Yes | We collect and annotate two datasets for evaluating the performance of our proposed system, one from the earthquake domain, and another from DDo S attack domain. ... We release the code and data sets of this paper at https://github. com/wangzq870305/joint_event_detection. |
| Dataset Splits | Yes | We randomly choose 10 events as training data for the earthquake domain, 80 events as training data for the DDo S domain, and the remaining events as testing data. |
| Hardware Specification | No | The paper describes the software components and training parameters, but does not specify any particular hardware (e.g., CPU, GPU models, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions using Adagrad for optimization, dropout, and training word embeddings using the Skip-gram algorithm, but it does not specify version numbers for these or other software libraries or frameworks (e.g., Python, PyTorch, TensorFlow versions). |
| Experiment Setup | Yes | Our training objective is to minimize the cross-entropy loss between the gold labels and predicted labels on those three tasks. We apply online training, adjusting model parameters using Adagrad [Duchi et al., 2011]. In order to avoid overfitting, dropout is used on word embeddings with a ratio of 0.2 [Hinton et al., 2012]. The size of the hidden layers Hd, Hc, and Hs are equally set to 32. We train word embeddings using the Skip-gram algorithm1, and fine-tune them during training. The size of word embeddings is 128. |