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..
Encoder-Decoder Based Unified Semantic Role Labeling with Label-Aware Syntax
Authors: Hao Fei, Fei Li, Bobo Li, Donghong Ji12794-12802
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical experiments show that our framework significantly outperforms all existing graph-based methods on the Co NLL09 and Universal Proposition Bank datasets. |
| Researcher Affiliation | Academia | Hao Fei, Fei Li, Bobo Li, Donghong Ji* Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University, Wuhan, China EMAIL, EMAIL |
| Pseudocode | No | The paper does not contain structured pseudocode or algorithm blocks. |
| Open Source Code | No | The paper does not provide concrete access to source code for the methodology described in this paper. It only links to the BERT repository, which is a third-party tool. |
| Open Datasets | Yes | We train and evaluate all models on two SRL benchmarks, including Co NLL09 (English), and Universal Proposition Bank (eight languages). |
| Dataset Splits | Yes | We employ the official training, development and test sets in each dataset. |
| Hardware Specification | No | The paper does not provide specific hardware details (e.g., GPU/CPU models, memory amounts) used for running its experiments. |
| Software Dependencies | No | The paper mentions using BERT (base-cased version) and an Adam optimizer, but does not provide specific software dependencies like programming language or library versions (e.g., Python 3.x, PyTorch 1.x). |
| Experiment Setup | Yes | In terms of hyper-parameters, since BERT is used, the size of word representations is 768. The size of POS tag embeddings is 50. We use a 3-layer LA-GCN with 350 hidden units, and the output size of LSTM decoder is 300. We adopt the Adam optimizer with an initial learning rate 2e-5, mini-batch size 16 and regularization weight 0.12. |