Ensembling Graph Predictions for AMR Parsing
Authors: Thanh Lam Hoang, Gabriele Picco, Yufang Hou, Young-Suk Lee, Lam Nguyen, Dzung Phan, Vanessa Lopez, Ramon Fernandez Astudillo
NeurIPS 2021 | Conference PDF | Archive PDF | Plain Text | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | To validate our approach, we carried out experiments in AMR parsing problems. The experimental results demonstrate that the proposed approach can combine the strength of state-of-the-art AMR parsers to create new predictions that are more accurate than any individual models in five standard benchmark datasets. |
| Researcher Affiliation | Industry | 1 IBM Research, Dublin, Ireland 2 IBM Research, Thomas J. Watson Research Center, Yorktown Heights, USA |
| Pseudocode | Yes | Algorithm 1: Graph ensemble with the Graphene algorithm. |
| Open Source Code | Yes | Source code is open-sourced1. 1https://github.com/IBM/graph_ensemble_learning |
| Open Datasets | Yes | Similarly to [Bevilacqua et al., 2021], we use five standard benchmark datasets [dat] to evaluate our approach. Table 1 shows the statistics of the datasets. AMR 2.0 and AMR 3.0 are divided into train, development and testing sets and we use them for in-distribution evaluation in Section 4.2. (...) AMR benchmark datasets. https://amr.isi.edu/download.html. |
| Dataset Splits | Yes | Table 1: Benchmark datasets. (...) For AMR 2.0 and 3.0, the models are trained on the training dataset, validated on the development dataset. We report results on testing sets in the in-distribution evaluation. |
| Hardware Specification | Yes | In all experiments, we used a Tesla GPU V100 for model training and used 8 CPUs for making an ensemble. |
| Software Dependencies | No | The paper mentions software components and models like BART, T5, ADAM optimization, and Stanford Core NLP, but does not provide specific version numbers for these dependencies. |
| Experiment Setup | Yes | The model is trained with 30 epochs. We use ADAM optimization with a learning rate of 1e-4 and a mini-batch size of 4. |