Dingzirui Wang

According to our database1, Dingzirui Wang authored at least 15 papers between 2022 and 2024.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

Legend:

Book 
In proceedings 
Article 
PhD thesis 
Dataset
Other 

Links

On csauthors.net:

Bibliography

2024
ConDA: state-based data augmentation for context-dependent text-to-SQL.
Int. J. Mach. Learn. Cybern., August, 2024

In-Context Transfer Learning: Demonstration Synthesis by Transferring Similar Tasks.
CoRR, 2024

FLEXTAF: Enhancing Table Reasoning with Flexible Tabular Formats.
CoRR, 2024

DAC: Decomposed Automation Correction for Text-to-SQL.
CoRR, 2024

Multi-Hop Table Retrieval for Open-Domain Text-to-SQL.
CoRR, 2024

A Survey of Table Reasoning with Large Language Models.
CoRR, 2024

Improving Demonstration Diversity by Human-Free Fusing for Text-to-SQL.
Proceedings of the Findings of the Association for Computational Linguistics: EMNLP 2024, 2024

Enhancing Numerical Reasoning with the Guidance of Reliable Reasoning Processes.
Proceedings of the 62nd Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers), 2024

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning of Large Language Models.
Proceedings of the Thirty-Eighth AAAI Conference on Artificial Intelligence, 2024

2023
UniSAr: a unified structure-aware autoregressive language model for text-to-SQL semantic parsing.
Int. J. Mach. Learn. Cybern., December, 2023

Exploring Equation as a Better Intermediate Meaning Representation for Numerical Reasoning.
CoRR, 2023

MultiSpider: Towards Benchmarking Multilingual Text-to-SQL Semantic Parsing.
Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence, 2023

2022
A Survey on Table-and-Text HybridQA: Concepts, Methods, Challenges and Future Directions.
CoRR, 2022

UniSAr: A Unified Structure-Aware Autoregressive Language Model for Text-to-SQL.
CoRR, 2022

Towards Knowledge-Intensive Text-to-SQL Semantic Parsing with Formulaic Knowledge.
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing, 2022


  Loading...