Conversational Semantic Role Labeling with Predicate-Oriented Latent Graph
Authors: Hao Fei, Shengqiong Wu, Meishan Zhang, Yafeng Ren, Donghong Ji
IJCAI 2022 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | 5 Experimentation. Table 1: Main results on three datasets. Table 2: Ablation results on Du Conv dataset. |
| Researcher Affiliation | Academia | 1Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, China 2Institute of Computing and Intelligence, Harbin Institute of Technology (Shenzhen), China 3Laboratory of Language and Artificial Intelligence, Guangdong University of Foreign Studies, China |
| Pseudocode | No | The paper describes methods using mathematical equations and textual explanations, but no formal pseudocode or algorithm blocks are provided. |
| Open Source Code | No | The paper does not provide any statement or link regarding the public availability of its source code. |
| Open Datasets | Yes | We conduct experiments on three CSRL datasets [Xu et al., 2021], including Du Conv, News Dialog and Personal Dialog |
| Dataset Splits | Yes | Du Conv has the 80%/10%/10% ratio of train/dev/test |
| Hardware Specification | No | The paper does not specify any hardware details such as GPU or CPU models used for the experiments. |
| Software Dependencies | No | The paper mentions software components like BERT, Dia BERT, GCN, and Adam optimizer, but no specific version numbers are provided for reproducibility. |
| Experiment Setup | Yes | GCN hidden size is set as 350. We adopt Adam as the optimizer with an initial learning rate of 5e-4 with weight decay of 1e-5. The initial α value is 1.5. To alleviate overfitting, we use a dropout rate of 0.5 on the input layer and the output layer. |