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..
Symbolic Priors for RNN-based Semantic Parsing
Authors: Chunyang Xiao, Marc Dymetman, Claire Gardent
IJCAI 2017 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | We test our method on an extension of the Overnight dataset and show that it not only strongly improves over an RNN baseline, but also outperforms non-RNN models based on rich sets of hand-crafted features. |
| Researcher Affiliation | Collaboration | Chunyang Xiao Marc Dymetman Xerox Research Centre Europe chunyang.xiao,EMAIL Claire Gardent CNRS, LORIA, UMR 7503 EMAIL |
| Pseudocode | No | The paper does not contain pseudocode or a clearly labeled algorithm block. |
| Open Source Code | No | The paper provides a GitHub link (https://github.com/chunyangx/overnight) for the extended Overnight+ dataset and mentions using a library from Wilker Aziz (https://github.com/wilkeraziz/pcfg-sampling), but it does not provide a direct link or explicit statement about the open-sourcing of the code for their own proposed methodology. |
| Open Datasets | Yes | we release an extended Overnight+ dataset.4 https://github.com/chunyangx/overnight |
| Dataset Splits | Yes | First, we group all the data and propose a new split. This split makes a 80%-20% random split over all the LFs and keeps the 20% LFs (together with their corresponding utterances) as test and the remaining 80% as training. For each domain, we also add new named entities into the knowledge base and create a new development set and test set containing those new named entities. |
| Hardware Specification | No | The paper does not provide specific details about the hardware used to run the experiments (e.g., GPU/CPU models, memory specifications). |
| Software Dependencies | No | The paper mentions using LSTM and MLP components, and references [Xiao et al., 2016] for neural network architecture and a library by Wilker Aziz for intersection algorithms. However, it does not provide specific version numbers for any software libraries or dependencies. |
| Experiment Setup | Yes | We concatenate ut and ub and pass the concatenated vector to a two-layer MLP for the ο¬nal prediction. At test time, we use a uniform-cost search algorithm [Russell and Norvig, 2003] to produce the DS with the highest probability. All the models are trained for 30 epochs. |