44 citations · 125 across the 19 of their papers we have counts for
6 papers · 1 filter
STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing
Zefeng Cai, Xiangyu Li, Binyuan Hui +8
In this paper, we propose a novel SQL guided pre-training framework STAR for context-dependent text-to-SQL parsing, which leverages contextual information to enrich natural languag…
SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers
Bowen Qin, Lihan Wang, Binyuan Hui +7
This paper aims to improve the performance of text-to-SQL parsing by exploring the intrinsic uncertainties in the neural network based approaches (called SUN). From the data uncert…
SPACE-3: Unified Dialog Model Pre-training for Task-Oriented Dialog Understanding and Generation
Wanwei He, Yinpei Dai, Min Yang +4
Recently, pre-training methods have shown remarkable success in task-oriented dialog (TOD) systems. However, most existing pre-trained models for TOD focus on either dialog underst…
SPACE-2: Tree-Structured Semi-Supervised Contrastive Pre-training for Task-Oriented Dialog Understanding
Wanwei He, Yinpei Dai, Binyuan Hui +6
Pre-training methods with contrastive learning objectives have shown remarkable success in dialog understanding tasks. However, current contrastive learning solely considers the se…
A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions
Bowen Qin, Binyuan Hui, Lihan Wang +9
Text-to-SQL parsing is an essential and challenging task. The goal of text-to-SQL parsing is to convert a natural language (NL) question to its corresponding structured query langu…
Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing
Lihan Wang, Bowen Qin, Binyuan Hui +7
The importance of building text-to-SQL parsers which can be applied to new databases has long been acknowledged, and a critical step to achieve this goal is schema linking, i.e., p…