Constructing Narrative Event Evolutionary Graph for Script Event Prediction
Authors: Zhongyang Li, Xiao Ding, Ting Liu
IJCAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on widely used New York Times corpus demonstrate that our model significantly outperforms state-of-the-art baseline methods, by using standard multiple choice narrative cloze evaluation. |
| Researcher Affiliation | Academia | Research Center for Social Computing and Information Retrieval, Harbin Institute of Technology {zyli, xding, tliu}@ir.hit.edu.cn |
| Pseudocode | No | The paper provides mathematical equations for the model but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | The data and code are released at https://github.com/eecrazy/ Constructing NEEG IJCAI 2018. |
| Open Datasets | Yes | Following Granroth-Wilding and Clark [2016], we extract event chains from the New York Times portion of the Gigaword corpus. |
| Dataset Splits | Yes | Table 1: Statistics of our datasets. Training: 140,331 #Chains for SGNN, Development: 10,000 #Chains for SGNN, Test: 10,000 #Chains for SGNN. |
| Hardware Specification | No | The paper does not specify any hardware details (e.g., CPU, GPU models, memory) used for the experiments. |
| Software Dependencies | No | The C&C tools [Curran et al., 2007] are used for POS tagging and dependency parsing, and Open NLP is used for phrase structure parsing and coreference resolution. No specific version numbers for these tools are provided. |
| Experiment Setup | Yes | All the hyperparameters are tuned on the development set, and we use margin loss as the objective function... The margin is the margin loss function parameter, which is set to 0.015. Θ is the set of model parameters. λ is the parameter for L2 regularization, which is set to 0.00001. The learning rate is 0.0001, batch size is 1000, and recurrent times K is 2. We use Deep Walk algorithm [Perozzi et al., 2014] to train embeddings for predicate-GR... and use the Skipgram algorithm [Mikolov et al., 2013] to train embeddings for arguments a0, a1, a2... The embedding dimension d is 128. The model parameters are optimized by the RMSprop algorithm. Early stopping is used to judge when to stop the training loop. |