87 citations · 179 across the 14 of their papers we have counts for
14 papers
Unifying Structured Data as Graph for Data-to-Text Pre-Training
Shujie Li, Liang Li, Ruiying Geng +8
Data-to-text (D2T) generation aims to transform structured data into natural language text. Data-to-text pre-training has proved to be powerful in enhancing D2T generation and yiel…
CATS: A Pragmatic Chinese Answer-to-Sequence Dataset with Large Scale and High Quality
Liang Li, Ruiying Geng, Chengyang Fang +6
There are three problems existing in the popular data-to-text datasets. First, the large-scale datasets either contain noise or lack real application scenarios. Second, the dataset…
Can LLM Already Serve as A Database Interface? A BIg Bench for Large-Scale Database Grounded Text-to-SQLs
Jinyang Li, Binyuan Hui, Ge Qu +15
Text-to-SQL parsing, which aims at converting natural language instructions into executable SQLs, has gained increasing attention in recent years. In particular, Codex and ChatGPT…
Plan-then-Seam: Towards Efficient Table-to-Text Generation
Liang Li, Ruiying Geng, Chengyang Fang +4
Table-to-text generation aims at automatically generating text to help people conveniently obtain salient information in tables. Recent works explicitly decompose the generation pr…
Graph-to-Text Generation with Dynamic Structure Pruning
Liang Li, Ruiying Geng, Bowen Li +4
Most graph-to-text works are built on the encoder-decoder framework with cross-attention mechanism. Recent studies have shown that explicitly modeling the input graph structure can…
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…
SSQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers
Binyuan Hui, Ruiying Geng, Lihan Wang +4
The task of converting a natural language question into an executable SQL query, known as text-to-SQL, is an important branch of semantic parsing. The state-of-the-art graph-based…
Linking-Enhanced Pre-Training for Table Semantic Parsing
Bowen Qin, Lihan Wang, Binyuan Hui +5
Recently pre-training models have significantly improved the performance of various NLP tasks by leveraging large-scale text corpora to improve the contextual representation abilit…
Relational Learning with Gated and Attentive Neighbor Aggregator for Few-Shot Knowledge Graph Completion
Guanglin Niu, Yang Li, Chengguang Tang +7
Aiming at expanding few-shot relations' coverage in knowledge graphs (KGs), few-shot knowledge graph completion (FKGC) has recently gained more research interests. Some existing mo…
Improving Text-to-SQL with Schema Dependency Learning
Binyuan Hui, Xiang Shi, Ruiying Geng +4
Text-to-SQL aims to map natural language questions to SQL queries. The sketch-based method combined with execution-guided (EG) decoding strategy has shown a strong performance on t…
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
Binyuan Hui, Ruiying Geng, Qiyu Ren +7
Semantic parsing has long been a fundamental problem in natural language processing. Recently, cross-domain context-dependent semantic parsing has become a new focus of research. C…
Dynamic Memory Induction Networks for Few-Shot Text Classification
Ruiying Geng, Binhua Li, Yongbin Li +2
This paper proposes Dynamic Memory Induction Networks (DMIN) for few-shot text classification. The model utilizes dynamic routing to provide more flexibility to memory-based few-sh…
Semantic Graph Convolutional Network for Implicit Discourse Relation Classification
Yingxue Zhang, Ping Jian, Fandong Meng +3
Implicit discourse relation classification is of great importance for discourse parsing, but remains a challenging problem due to the absence of explicit discourse connectives comm…
Induction Networks for Few-Shot Text Classification
Ruiying Geng, Binhua Li, Yongbin Li +3
Text classification tends to struggle when data is deficient or when it needs to adapt to unseen classes. In such challenging scenarios, recent studies have used meta-learning to s…