End-to-end Semantic Role Labeling with Neural Transition-based Model
Authors: Hao Fei, Meishan Zhang, Bobo Li, Donghong Ji12803-12811
AAAI 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Experimental results on Co NLL09 and Universal Proposition Bank show that our final model can produce state-of-the-art performance, and meanwhile keeps highly efficient in decoding. We also conduct detailed experimental analysis for a deep understanding of our proposed model. |
| Researcher Affiliation | Academia | 1 Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China 2 Department of School of New Media and Communication, Tianjin University, Tianjin, China {hao.fei, boboli, dhji}@whu.edu.cn, mason.zms@gmail.com |
| Pseudocode | No | The paper describes the transition system and actions in prose and with an example table (Figure 2(a)), but does not include a formal pseudocode block or algorithm figure. |
| Open Source Code | Yes | Our codes are open at https://github.com/scofield7419/Transition SRL. |
| Open Datasets | Yes | We employ two dependency-based SRL benchmarks: Co NLL09 English, and Universal Proposition Bank (UPB) for other total seven languages. |
| Dataset Splits | Yes | Each dataset comes with its own train, develop and test sets. |
| Hardware Specification | No | No specific hardware details (like GPU models, CPU types, or memory specifications) used for running experiments are mentioned in the paper. |
| Software Dependencies | No | The paper mentions software like 'fasttext', 'ELMo', 'BERT', and 'XLNet', but does not provide specific version numbers for these or any other software dependencies. It only specifies 'base-cased-version' for BERT and 'base-version' for XLNet, which are model variants, not software versions. |
| Experiment Setup | Yes | The hidden sizes in Bi LSTM, Tree LSTM and Stack-LSTM are 200. We use the two layer version of Bi LSTM, Stack-LSTM. The dimension of transition states is 150 universally. We adopt the Adam optimizer with initial learning rate of 1e-5. ζ is 0.2, and beam size B is 32, according to our developing experiments. We train the model by mini-batch size in [16,32] with early-stop strategy. |