Notice: The reproducibility variables underlying each score are classified using an automated LLM-based pipeline, validated against a manually labeled dataset. LLM-based classification introduces uncertainty and potential bias; scores should be interpreted as estimates. Full accuracy metrics and methodology are described in Coakley et alK. L. Coakley, T. Snelleman, H. Hoos, and O. E. Gundersen, "The embrace of open science: An analysis of a decade of AI research and 56 800 conference papers," Under Review, 2026..
Strongly Incremental Constituency Parsing with Graph Neural Networks
Authors: Kaiyu Yang, Jia Deng
NeurIPS 2020 | Venue PDF | 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 EMAIL Jia Deng Princeton University EMAIL |
| 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. |