Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE represents the question and DB schema in a tagged sequence where a subset of the fields are augmented with cell values mentioned in the question. The hybrid sequence is encoded by BERT with minimal subsequent layers and the text-DB contextualization is realized via the fine-tuned deep attention in BERT. Combined with a pointer-generator decoder with schema-consistency driven search space pruning, BRIDGE attained state-of-the-art performance on popular cross-DB text-to-SQL benchmarks, Spider (71.1\% dev, 67.5\% test with ensemble model) and WikiSQL (92.6\% dev, 91.9\% test). Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks. Our implementation is available at \url{https://github.com/salesforce/TabularSemanticParsing}.
Code (2)
Tasks
DecoderDeep AttentionSemantic ParsingText to SQLText-To-SQLMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
SeFNet: Bridging Tabular Datasets with Semantic Feature Nets
Machine learning applications cover a wide range of predictive tasks in which tabular datasets play a significant role. However, although they often address similar problems, tabular datasets are typically treated as sta…
Meta-LearningSemantic SimilaritySemantic Textual SimilarityiLTM: Integrated Large Tabular Model
Tabular data underpins decisions across science, industry, and public services. Despite rapid progress, advances in deep learning have not fully carried over to the tabular domain, where gradient-boosted decision trees (…
Enhancing Tabular Learners with Context-Aware Semantic Embeddings
While modern tabular learners excel at capturing statistical patterns, they frequently operate in a semantic vacuum, treating textual features as discrete symbols, ignoring the rich semantics inherent in feature names or…
Transfer Learning with Deep Tabular Models
Recent work on deep learning for tabular data demonstrates the strong performance of deep tabular models, often bridging the gap between gradient boosted decision trees and neural networks. Accuracy aside, a major advant…
Medical DiagnosisTransfer LearningReTabAD: A Benchmark for Restoring Semantic Context in Tabular Anomaly Detection
In tabular anomaly detection (AD), textual semantics often carry critical signals, as the definition of an anomaly is closely tied to domain-specific context. However, existing benchmarks provide only raw data points wit…
Anomaly Detection