Log In Sign Up

Translating Natural Language to SQL using Pointer-Generator Networks and How Decoding Order Matters

by   Denis Lukovnikov, et al.

Translating natural language to SQL queries for table-based question answering is a challenging problem and has received significant attention from the research community. In this work, we extend a pointer-generator and investigate the order-matters problem in semantic parsing for SQL. Even though our model is a straightforward extension of a general-purpose pointer-generator, it outperforms early works for WikiSQL and remains competitive to concurrently introduced, more complex models. Moreover, we provide a deeper investigation of the potential order-matters problem that could arise due to having multiple correct decoding paths, and investigate the use of REINFORCE as well as a dynamic oracle in this context.


page 1

page 2

page 3

page 4


TableQA: a Large-Scale Chinese Text-to-SQL Dataset for Table-Aware SQL Generation

Parsing natural language to corresponding SQL (NL2SQL) with data driven ...

IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles

We present a sequence-to-action parsing approach for the natural languag...

Robust Text-to-SQL Generation with Execution-Guided Decoding

We consider the problem of neural semantic parsing, which translates nat...

Table2answer: Read the database and answer without SQL

Semantic parsing is the task of mapping natural language to logic form. ...

Recent Advances in Text-to-SQL: A Survey of What We Have and What We Expect

Text-to-SQL has attracted attention from both the natural language proce...

T5QL: Taming language models for SQL generation

Automatic SQL generation has been an active research area, aiming at str...

Faster and Better Grammar-based Text-to-SQL Parsing via Clause-level Parallel Decoding and Alignment Loss

Grammar-based parsers have achieved high performance in the cross-domain...