SParC: Cross-Domain Semantic Parsing in Context

@inproceedings{Yu2019SParCCS,
  title={SParC: Cross-Domain Semantic Parsing in Context},
  author={Tao Yu and Rui Zhang and Michihiro Yasunaga and Y. Tan and Xi Victoria Lin and Suyi Li and H. Er and Irene Z Li and B. Pang and Tao Chen and Emily Ji and Shreya Dixit and David Proctor and Sungrok Shim and Jonathan Kraft and V. Zhang and Caiming Xiong and R. Socher and Dragomir R. Radev},
  booktitle={ACL},
  year={2019}
}
We present SParC, a dataset for cross-domainSemanticParsing inContext that consists of 4,298 coherent question sequences (12k+ individual questions annotated with SQL queries. [...] Key Method We experiment with two state-of-the-art text-to-SQL models adapted to the context-dependent, cross-domain setup. The best model obtains an exact match accuracy of 20.2% over all questions and less than10% over all interaction sequences, indicating that the cross-domain setting and the con-textual phenomena of the dataset…Expand
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
TLDR
This work presents BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing that effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks. Expand
Dynamic Hybrid Relation Network for Cross-Domain Context-Dependent Semantic Parsing
TLDR
This paper presents a dynamic graph framework that is capable of effectively modelling contextual utterances, tokens, database schemas, and their complicated interaction as the conversation proceeds, and employs a dynamic memory decay mechanism that incorporates inductive bias to integrate enriched contextual relation representation. Expand
Exploring Unexplored Generalization Challenges for Cross-Database Semantic Parsing
TLDR
This work re-purpose eight semantic parsing datasets that have been well-studied in the setting where in-domain training data is available, and instead use them as additional evaluation data for XSP systems instead, to uncovers several generalization challenges for cross-database semantic parsing. Expand
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
TLDR
The interaction history is utilized by editing the previous predicted query to improve the generation quality of SQL queries and the benefit of editing compared with the state-of-the-art baselines which generate SQL from scratch is evaluated. Expand
CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
TLDR
CoSQL is presented, a corpus for building cross-domain, general-purpose database (DB) querying dialogue systems that includes SQL-grounded dialogue state tracking, response generation from query results, and user dialogue act prediction and a set of strong baselines are evaluated. Expand
IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation
TLDR
The proposed database schema interaction graph encoder uses a gate mechanism to weigh the importance of different vocabularies and then make the prediction of SQL tokens and achieves new state-of-the-art results on the two datasets. Expand
Neural Approaches for Natural Language Interfaces to Databases: A Survey
TLDR
This survey focuses on the key design decisions behind current state of the art neural approaches, which are group into encoder and decoder improvements, and highlights the three most important directions, namely linking question tokens to database schema elements, better architectures for encoding the textual query taking into account the schema, and improved generation of structured queries using autoregressive neural models. Expand
Photon: A Robust Cross-Domain Text-to-SQL System
TLDR
PHOTON is presented, a robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined and effectively improves the robustness of text-to-SQL system against untranslatable user input. Expand
ValueNet: A Neural Text-to-SQL Architecture Incorporating Values
TLDR
The main idea of the approach is to use not only metadata information about the underlying database but also information on the base data as input for the neural network architecture for the text-to-SQL system incorporating values on the challenging Spider dataset. Expand
Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing
TLDR
Two kinds of interaction states are defined based on schema items and SQL keywords separately, and a relational graph neural network and a non-linear layer are designed to update the representations of these two states respectively. Expand
...
1
2
3
4
5
...

References

SHOWING 1-10 OF 40 REFERENCES
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
TLDR
This work defines a new complex and cross-domain semantic parsing and text-to-SQL task so that different complicated SQL queries and databases appear in train and test sets and experiments with various state-of-the-art models show that Spider presents a strong challenge for future research. Expand
IncSQL: Training Incremental Text-to-SQL Parsers with Non-Deterministic Oracles
TLDR
A sequence-to-action parsing approach for the natural language to SQL task that incrementally fills the slots of a SQL query with feasible actions from a pre-defined inventory and sets a new state-of-the-art performance at an execution accuracy of 87.1%. Expand
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
TLDR
Experimental results show that SyntaxSQLNet can handle a significantly greater number of complex SQL examples than prior work, outperforming the previous state-of-the-art model by 9.5% in exact matching accuracy. Expand
Compositional Semantic Parsing on Semi-Structured Tables
TLDR
This paper proposes a logical-form driven parsing algorithm guided by strong typing constraints and shows that it obtains significant improvements over natural baselines and is made publicly available. Expand
QuAC: Question Answering in Context
TLDR
QuAC introduces challenges not found in existing machine comprehension datasets: its questions are often more open-ended, unanswerable, or only meaningful within the dialog context, as it shows in a detailed qualitative evaluation. Expand
Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
TLDR
This work proposes Seq2 SQL, a deep neural network for translating natural language questions to corresponding SQL queries, and releases WikiSQL, a dataset of 80654 hand-annotated examples of questions and SQL queries distributed across 24241 tables fromWikipedia that is an order of magnitude larger than comparable datasets. Expand
TypeSQL: Knowledge-Based Type-Aware Neural Text-to-SQL Generation
TLDR
This paper presents a novel approach TypeSQL which formats the problem as a slot filling task in a more reasonable way and utilizes type information to better understand rare entities and numbers in the questions. Expand
Search-based Neural Structured Learning for Sequential Question Answering
TLDR
This work proposes a novel dynamic neural semantic parsing framework trained using a weakly supervised reward-guided search that effectively leverages the sequential context to outperform state-of-the-art QA systems that are designed to answer highly complex questions. Expand
Improving Text-to-SQL Evaluation Methodology
TLDR
It is shown that the current division of data into training and test sets measures robustness to variations in the way questions are asked, but only partially tests how well systems generalize to new queries, and proposes a complementary dataset split for evaluation of future work. Expand
Towards a theory of natural language interfaces to databases
TLDR
This paper proves that, for a broad class of semantically tractable natural language questions, Precise is guaranteed to map each question to the corresponding SQL query, and shows that Precise compares favorably with Mooney's learning NLI and with Microsoft's English Query product. Expand
...
1
2
3
4
...