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Latest papers without code

ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser

10 Apr 2021

Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query.

SEMANTIC PARSING TEXT-TO-SQL

NL-EDIT: Correcting semantic parse errors through natural language interaction

26 Mar 2021

We present NL-EDIT, a model for interpreting natural language feedback in the interaction context to generate a sequence of edits that can be applied to the initial parse to correct its errors.

SEMANTIC PARSING TEXT-TO-SQL

Self-supervised Text-to-SQL Learning with Header Alignment Training

11 Mar 2021

Since we can leverage a large amount of unlabeled data without any human supervision to train a model and transfer the knowledge to target tasks, self-supervised learning is a de-facto component for the recent success of deep learning in various fields.

SELF-SUPERVISED LEARNING TEXT-TO-SQL

Improving Text-to-SQL with Schema Dependency Learning

7 Mar 2021

In this paper, we present the Schema Dependency guided multi-task Text-to-SQL model (SDSQL) to guide the network to effectively capture the interactions between questions and schemas.

TEXT-TO-SQL

Data Augmentation with Hierarchical SQL-to-Question Generation for Cross-domain Text-to-SQL Parsing

3 Mar 2021

Data augmentation has attracted a lot of research attention in the deep learning era for its ability in alleviating data sparseness.

DATA AUGMENTATION QUESTION GENERATION SQL PARSING TEXT-TO-SQL

GP: Context-free Grammar Pre-training for Text-to-SQL Parsers

25 Jan 2021

A new method for Text-to-SQL parsing, Grammar Pre-training (GP), is proposed to decode deep relations between question and database.

SQL PARSING TEXT-TO-SQL

Optimizing Deeper Transformers on Small Datasets: An Application on Text-to-SQL Semantic Parsing

30 Dec 2020

Due to the common belief that training deep transformers from scratch requires large datasets, people usually only use shallow and simple additional layers on top of pre-trained models during fine-tuning on small datasets.

SEMANTIC PARSING TEXT-TO-SQL

MT-Teql: Evaluating and Augmenting Consistency of Text-to-SQL Models with Metamorphic Testing

21 Dec 2020

Envisioning the general difficulty for text-to-SQL models to preserve prediction consistency against linguistic and schema variations, we propose MT-Teql, a Metamorphic Testing-based framework for systematically evaluating and augmenting the consistency of TExt-to-SQL models.

TEXT-TO-SQL

Mention Extraction and Linking for SQL Query Generation

EMNLP 2020

On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot-filling approach by building several dedicated models for each type of slots.

SLOT FILLING TEXT-TO-SQL

Learning Contextual Representations for Semantic Parsing with Generation-Augmented Pre-Training

18 Dec 2020

Most recently, there has been significant interest in learning contextual representations for various NLP tasks, by leveraging large scale text corpora to train large neural language models with self-supervised learning objectives, such as Masked Language Model (MLM).

LANGUAGE MODELLING SELF-SUPERVISED LEARNING SEMANTIC PARSING TEXT-TO-SQL