Search Results for author:
Found 13 papers, 8 papers with code
CSS: A Large-scale Cross-schema Chinese Text-to-SQL Medical Dataset
Furthermore, we present CSS, a large-scale CrosS-Schema Chinese text-to-SQL dataset, to carry on corresponding studies.
On the Structural Generalization in Text-to-SQL
Exploring the generalization of a text-to-SQL parser is essential for a system to automatically adapt the real-world databases.
TIE: Topological Information Enhanced Structural Reading Comprehension on Web Pages
Recently, the structural reading comprehension (SRC) task on web pages has attracted increasing research interests.
Few-Shot NLU with Vector Projection Distance and Abstract Triangular CRF
In this paper, we propose to improve prototypical networks with vector projection distance and abstract triangular Conditional Random Field (CRF) for the few-shot NLU.
Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL
A dual learning approach is also proposed for the utterance rewrite model to address the data sparsity problem.
LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations
This work aims to tackle the challenging heterogeneous graph encoding problem in the text-to-SQL task.
ShadowGNN: Graph Projection Neural Network for Text-to-SQL Parser
Given a database schema, Text-to-SQL aims to translate a natural language question into the corresponding SQL query.
Vector Projection Network for Few-shot Slot Tagging in Natural Language Understanding
Few-shot slot tagging becomes appealing for rapid domain transfer and adaptation, motivated by the tremendous development of conversational dialogue systems.
Line Graph Enhanced AMR-to-Text Generation with Mix-Order Graph Attention Networks
We also adopt graph attention networks with higher-order neighborhood information to encode the rich structure in AMR graphs.
Unsupervised Dual Paraphrasing for Two-stage Semantic Parsing
One daunting problem for semantic parsing is the scarcity of annotation.
Jointly Encoding Word Confusion Network and Dialogue Context with BERT for Spoken Language Understanding
In this paper, a novel BERT based SLU model (WCN-BERT SLU) is proposed to encode WCNs and the dialogue context jointly.
Dual Learning for Semi-Supervised Natural Language Understanding
The framework is composed of dual pseudo-labeling and dual learning method, which enables an NLU model to make full use of data (labeled and unlabeled) through a closed-loop of the primal and dual tasks.
Semantic Parsing with Dual Learning
Semantic parsing converts natural language queries into structured logical forms.
