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..
Parsing as Pretraining
Authors: David Vilares, Michalina Strzyz, Anders Søgaard, Carlos Gómez-Rodríguez9114-9121
AAAI 2020 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | For evaluation, we use bracketing F1score and LAS, and analyze in-depth differences across representations for span lengths and dependency displacements. The overall results surpass existing sequence tagging parsers on the PTB (93.5%) and end-to-end EN-EWT UD (78.8%). |
| Researcher Affiliation | Collaboration | 1Universidade da Coru na, CITIC, Ciencias de la Computaci on y Tecnolog ıas de la Informaci on (CC&TI), A Coru na, Spain 2University of Copenhagen, Department of Computer Science, Copenhagen, Denmark 3Google Research, Berlin, Germany |
| Pseudocode | No | The paper describes the approach, including the mapping and postprocessing steps, and depicts the architecture in Figure 3, but does not present any formal pseudocode or algorithm blocks. |
| Open Source Code | Yes | The source code is accessible at https://github.com/aghie/parsing-as-pretraining. |
| Open Datasets | Yes | We use the English Penn Treebank (PTB) (Marcus, Santorini, and Marcinkiewicz 1993) for evaluation on constituent parsing, and the EN-EWT UD treebank (v2.2) for dependency parsing (Nivre and others 2017). |
| Dataset Splits | No | The paper mentions using 'train' and 'test' sets but does not explicitly provide details about a 'validation' dataset split or its proportions. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used for running experiments, such as CPU or GPU models. |
| Software Dependencies | No | The paper mentions using a 'pytorch wrapper' for BERT and building on the 'framework by Yang and Zhang (2018)' but does not provide specific version numbers for software dependencies such as PyTorch, Python, or CUDA. |
| Experiment Setup | Yes | For ff/lstm, the learning rate was set to 5e-4. |