343 citations · 500 across the 5 of their papers we have counts for
5 papers
KaggleDBQA: Realistic Evaluation of Text-to-SQL Parsers
Chia-Hsuan Lee, Oleksandr Polozov, Matthew Richardson
The goal of database question answering is to enable natural language querying of real-life relational databases in diverse application domains. Recently, large-scale datasets such…
NL-EDIT: Correcting semantic parse errors through natural language interaction
Ahmed Elgohary, Christopher Meek, Matthew Richardson +3
We study semantic parsing in an interactive setting in which users correct errors with natural language feedback. We present NL-EDIT, a model for interpreting natural language feed…
Structure-Grounded Pretraining for Text-to-SQL
Xiang Deng, Ahmed Hassan Awadallah, Christopher Meek +3
Learning to capture text-table alignment is essential for tasks like text-to-SQL. A model needs to correctly recognize natural language references to columns and values and to grou…
RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers
Bailin Wang, Richard Shin, Xiaodong Liu +2
When translating natural language questions into SQL queries to answer questions from a database, contemporary semantic parsing models struggle to generalize to unseen database sch…
DyNet: The Dynamic Neural Network Toolkit
Graham Neubig, Chris Dyer, Yoav Goldberg +22
We describe DyNet, a toolkit for implementing neural network models based on dynamic declaration of network structure. In the static declaration strategy that is used in toolkits l…