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Found 12 papers, 3 papers with code
``What Do You Mean by That?'' A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
Heterophily-Based Graph Neural Network for Imbalanced Classification
This leads to suboptimal performance of standard GNNs on imbalanced graphs.
T5-SR: A Unified Seq-to-Seq Decoding Strategy for Semantic Parsing
Translating natural language queries into SQLs in a seq2seq manner has attracted much attention recently.
Ranked #8 on
Text-To-SQL
on spider
Improving Semantic Matching through Dependency-Enhanced Pre-trained Model with Adaptive Fusion
Transformer-based pre-trained models like BERT have achieved great progress on Semantic Sentence Matching.
DABERT: Dual Attention Enhanced BERT for Semantic Matching
To alleviate this problem, we propose a novel Dual Attention Enhanced BERT (DABERT) to enhance the ability of BERT to capture fine-grained differences in sentence pairs.
Let Me Check the Examples: Enhancing Demonstration Learning via Explicit Imitation
Inspired by the human learning process, in this paper, we introduce Imitation DEMOnstration Learning (Imitation-Demo) to strengthen demonstration learning via explicitly imitating human review behaviour, which includes: (1) contrastive learning mechanism to concentrate on the similar demonstrations.
Pay More Attention to History: A Context Modelling Strategy for Conversational Text-to-SQL
Conversational text-to-SQL aims at converting multi-turn natural language queries into their corresponding SQL (Structured Query Language) representations.
Ranked #2 on
Text-To-SQL
on SParC
Small Changes Make Big Differences: Improving Multi-turn Response Selection in Dialogue Systems via Fine-Grained Contrastive Learning
The sequence representation plays a key role in the learning of matching degree between the dialogue context and the response.
r-GAT: Relational Graph Attention Network for Multi-Relational Graphs
To tackle this issue, we propose r-GAT, a relational graph attention network to learn multi-channel entity representations.
"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions.
DisenE: Disentangling Knowledge Graph Embeddings
Knowledge graph embedding (KGE), aiming to embed entities and relations into low-dimensional vectors, has attracted wide attention recently.
Investigating the Impact of Data Volume and Domain Similarity on Transfer Learning Applications
In this paper, we investigate the impact of target dataset size and source/target domain similarity on model performance through a series of experiments.
