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..
A Learning Error Analysis for Structured Prediction with Approximate Inference
Authors: Yuanbin Wu, Man Lan, Shiliang Sun, Qi Zhang, Xuanjing Huang
NeurIPS 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Empirical evaluations on text classification, sequential labelling and dependency parsing witness the success of approximate inference and the benefit of the proposed margin. |
| Researcher Affiliation | Academia | 1School of Computer Science and Software Engineering, East China Normal University 2Shanghai Key Laboratory of Multidimensional Information Processing 3School of Computer Science, Fudan University |
| Pseudocode | Yes | Algorithm 3: Structured perceptron with mρ. and Algorithm 4: Online PA with mρ. |
| Open Source Code | No | The paper references third-party tools like SVMmulticlass and MSTParser, but does not provide a link or statement about the authors' own open-source code for the described methodology. |
| Open Datasets | Yes | We report results on the 20 newsgroups corpus (18000 documents, 20 classes). We use the Co NLL 2000 dataset [Sang and Buchholz, 2000] for chunking and POS tagging, SIGHAN 2005 bake-off corpus (pku and msr) [Emerson, 2005] for word segmentation. |
| Dataset Splits | Yes | We take 20% of the training set as development set for tuning ρ (grid search in [0, 2] with step size 0.05). |
| Hardware Specification | No | The paper does not provide any specific details about the hardware used for the experiments. |
| Software Dependencies | Yes | The implementation is adapted from SVMmulticlass 3. |
| Experiment Setup | Yes | We take 20% of the training set as development set for tuning ρ (grid search in [0, 2] with step size 0.05). We use Algorithm 3 with 20 iterations and learning step 1. |