Syntax-Aware Neural Semantic Role Labeling
Authors: Qingrong Xia, Zhenghua Li, Min Zhang, Meishan Zhang, Guohong Fu, Rui Wang, Luo Si7305-7313
AAAI 2019 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experiments on the benchmark Co NLL-2005 dataset show that syntax-aware SRL approaches can effectively improve performance over a strong baseline with external word representations from ELMo. With the extra syntax-aware representations, our approaches achieve new state-of-the-art 85.6 F1 (single model) and 86.6 F1 (ensemble) on the test data |
| Researcher Affiliation | Collaboration | 1Institute of Artificial Intelligence, School of Computer Science and Technology, Soochow University, China 2School of Computer Science and Technology, Heilongjiang University, China, 3Alibaba Group, China |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithm listings. |
| Open Source Code | Yes | We implement the baseline and all the syntax-aware methods with Pytorch 0.3.04. github.com/KiroSummer/Syntax-aware-Neural-SRL |
| Open Datasets | Yes | Following previous works, we adopt the Co NLL-2005 dataset with the standard data split: sections 02-22 of the Wall Street Journal (WSJ) corpus as the training dataset |
| Dataset Splits | Yes | section 24 as the development dataset |
| Hardware Specification | No | The paper does not provide specific hardware details such as GPU models, CPU types, or memory specifications used for the experiments. |
| Software Dependencies | Yes | We implement the baseline and all the syntax-aware methods with Pytorch 0.3.04. |
| Experiment Setup | Yes | We adopt the Adadelta optimizer with learning rate ρ = 0.95 and ϵ = 1e 6, use a batchsize of 80, and clip gradients with norm larger than 1.0. All the embedding dimensions (word, predicate-indicator, syntactic label, pattern, and TPF) are set to 100. All models are trained for 500 iterations on the trained data and select the best iteration that has the peak performance on the dev data. |