Symbolic Priors for RNN-based Semantic Parsing
Authors: Chunyang Xiao, Marc Dymetman, Claire Gardent
IJCAI 2017 | Conference PDF | Archive PDF | Plain Text | 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,marc.dymetman@xerox.com Claire Gardent CNRS, LORIA, UMR 7503 claire.gardent@loria.fr |
| 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 final 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. |