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..
Moment Distributionally Robust Tree Structured Prediction
Authors: Yeshu Li, Danyal Saeed, Xinhua Zhang, Brian Ziebart, Kevin Gimpel
NeurIPS 2022 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We evaluate its empirical effectiveness on dependency parsing benchmarks. |
| Researcher Affiliation | Academia | Yeshu Li Danyal Saeed Xinhua Zhang Brian D. Ziebart Department of Computer Science University of Illinois at Chicago EMAIL Kevin Gimpel Toyota Technological Institute at Chicago EMAIL |
| Pseudocode | No | The paper describes algorithms such as "double oracle" and "ADMM" verbally and references existing algorithms, but it does not provide any explicitly labeled pseudocode blocks or algorithms. |
| Open Source Code | Yes | Our code is publicly available at https://github.com/Daniel Leee/drtreesp. |
| Open Datasets | Yes | We adopt three public datasets, the English Penn Treebank (PTB v3.0) [Marcus et al., 1993], the Penn Chinese Treebank (CTB v5.1) [Xue et al., 2002] and the Universal Dependencies (UD v2.3) [Nivre et al., 2016]. |
| Dataset Splits | Yes | in each run, we randomly draw m {10, 50, 100, 1000} samples without replacement from the training set and keep the original validation and test sets. The optimal hyperparameters and parameters are chosen based on the validation set. |
| Hardware Specification | Yes | All experiments are conducted on a computer with an Intel Core i7 CPU (2.7 GHz) and an NVIDIA Tesla P100 GPU (16 GB). |
| Software Dependencies | No | The paper states: "We implement our methods in Python and C2. We leverage the implementations in Su Par3 [Zhang et al., 2020] for the baseline." However, it does not provide specific version numbers for Python, C, or Su Par, which are required for a reproducible description of software dependencies. |
| Experiment Setup | Yes | The optimal hyperparameters and parameters are chosen based on the validation set. For fair comparisons, all the models are run with CPU only, with a batch size of 200. All the methods achieve their optimal validation set performance in 150-300 steps. We conduct sensitivity analysis by varying ยต and ฮป on UD Dutch with 100 training samples. |