Strongly Incremental Constituency Parsing with Graph Neural Networks
Authors: Kaiyu Yang, Jia Deng
NeurIPS 2020 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate our parser on Penn Treebank (PTB) and Chinese Treebank (CTB). On PTB, it outperforms existing parsers trained with only constituency trees; and it performs on par with state-of-the-art parsers that use dependency trees as additional training data. On CTB, our parser establishes a new state of the art. |
| Researcher Affiliation | Academia | Kaiyu Yang Princeton University kaiyuy@cs.princeton.edu Jia Deng Princeton University jiadeng@cs.princeton.edu |
| Pseudocode | No | The paper does not contain any explicit pseudocode blocks or algorithms labeled as such. |
| Open Source Code | Yes | Code is available at https://github.com/princeton-vl/ attach-juxtapose-parser. |
| Open Datasets | Yes | We evaluate our model for constituency parsing on two standard benchmarks: Chinese Treebank 5.1 (CTB) [46] and the Wall Street Journal part of Penn Treebank (PTB) [24]. |
| Dataset Splits | Yes | PTB consists of 39,832 training examples; 1,700 validation examples; and 2,416 testing examples. Whereas CTB consists of 17,544/352/348 examples for training/validation/testing respectively. |
| Hardware Specification | Yes | The model is implemented in Py Torch [29] and takes 2 3 days to train on a single Nvidia Ge Force GTX 2080 Ti GPU. These experiments were run on machines with 2 CPUs, 16GB memory, and one GTX 2080 Ti GPU. |
| Software Dependencies | No | The paper mentions that the model is 'implemented in Py Torch [29]' but does not provide a specific version number for PyTorch or any other software dependency. |
| Experiment Setup | Yes | Model parameters are optimized using RMSProp [40] with a batch size of 32. We decrease the learning rate by a factor of 2 when the best validation F1 score plateaus. The hyperparameters for each model are in the supplementary materials. |