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..
Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training
Authors: Peng Shi, Patrick Ng, Zhiguo Wang, Henghui Zhu, Alexander Hanbo Li, Jun Wang, Cicero Nogueira dos Santos, Bing Xiang13806-13814
AAAI 2021 | Venue PDF | LLM Run Details
| Reproducibility Variable | Result | LLM Response |
|---|---|---|
| Research Type | Experimental | Based on experimental results, neural semantic parsers that leverage GAP MODEL as a representation encoder obtain new state-of-the-art results on both SPIDER and CRITERIA-TO-SQL benchmarks. |
| Researcher Affiliation | Collaboration | Peng Shi1 , Patrick Ng2, Zhiguo Wang2, Henghui Zhu2, Alexander Hanbo Li2, Jun Wang2, Cicero Nogueira dos Santos2, Bing Xiang2 1 University of Waterloo, 2 AWS AI Labs EMAIL, EMAIL |
| Pseudocode | No | The paper describes the model architecture and training tasks conceptually but does not include any pseudocode or algorithm blocks. |
| Open Source Code | Yes | Our code is public for future work. 2https://github.com/awslabs/gap-text2sql |
| Open Datasets | Yes | SPIDER: SPIDER dataset (Yu et al. 2018) is a text-to-SQL dataset with 10,181 annotated parallel utterance-database SQL triples. (...) CRITERIA-TO-SQL: (...) The dataset contains 2003 annotated examples, and the evaluation metrics are the SQL accuracy and execution accuracy. |
| Dataset Splits | Yes | After finetuning BART, the model can generate high-quality utterances logically consistent with the input SQL, achieving a 0.1934 BLEU score on the development set. (...) After fine-tuning, the model achieves 0.1821 BLEU score on the development set. (...) Table 2 shows the end-to-end results on the public development set and hidden test set of SPIDER. |
| Hardware Specification | No | The paper does not specify the hardware (e.g., GPU models, CPU types, memory) used for running the experiments. |
| Software Dependencies | No | The paper mentions models like BART and BERT but does not provide specific version numbers for any software libraries, frameworks, or dependencies. |
| Experiment Setup | Yes | In the pre-training, we train our GAP MODEL with the underlying transformers initialized with BART (Lewis et al. 2019) model. During the fine-tuning phase, we only leverage the encoder component of the GAP MODEL with 12-layer transformers as the encoder for the semantic parsers. (...) We use the standard MLM objective, with a masking rate of 35% sub-tokens in the whole input sequence, including the utterance and schema. |