A Neural Transition-Based Approach for Semantic Dependency Graph Parsing
Authors: Yuxuan Wang, Wanxiang Che, Jiang Guo, Ting Liu
AAAI 2018 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our parser on the Sem Eval-2016 Task 9 dataset (Chinese) and the Sem Eval-2015 Task 18 dataset (English). On both benchmark datasets, we obtain superior or comparable results to the best performing systems. Our parser can be further improved with a simple ensemble mechanism, resulting in the state-of-the-art performance. |
| Researcher Affiliation | Academia | Yuxuan Wang, Wanxiang Che, Jiang Guo, Ting Liu Research Center for Social Computing and Information Retrieval Harbin Institute of Technology, Harbin, China {yxwang, car, jguo, tliu}@ir.hit.edu.cn |
| Pseudocode | Yes | Table 1: Transitions deļ¬ned in the list-based arc-eager algorithm (Choi and Mc Callum 2013). This table presents structured steps for the transition system. |
| Open Source Code | Yes | Our system will be publicly available at https://github.com/HITalexwang/lstm-sdparser. |
| Open Datasets | Yes | For Chinese, we use the Sem Eval-2016 Task 9 as our testbed. ... For English, we conduct experiments on the English part of Sem Eval-2015 Task 18 closed track (Oepen et al. 2015). |
| Dataset Splits | Yes | For English, we conduct experiments on the English part of Sem Eval-2015 Task 18 closed track (Oepen et al. 2015). We use the same data split as previous work (Almeida and Martins 2015; Du et al. 2015), with 33,964 training sentences (WSJ 00-19), 1,692 development sentences ( 20), 1,410 in-domain testing sentences ( 21) and 1,849 out-of-domain testing sentences from the Brown Corpus. |
| Hardware Specification | No | The paper mentions using DyNet for implementation but does not specify any hardware details like GPU or CPU models used for experiments. |
| Software Dependencies | No | The paper mentions 'Dy Net (Neubig et al. 2017)' as the neural model implementation library but does not provide a specific version number. |
| Experiment Setup | Yes | The Stack-LSTMs and Bi-LSTM have two layers while the Tree-LSTM has one. The input and hidden dimensions of Stack-LSTM, Bi-LSTM and Tree-LSTM are 200. The learned word embedding size dwt = 100, POS tag, relation and transition embedding size are all 50. |