Photon: A Robust Cross-Domain Text-to-SQL System

• URL: http://arxiv.org/abs/2007.15280v2
• Date: Mon, 3 Aug 2020 08:59:06 GMT
• Title: Photon: A Robust Cross-Domain Text-to-SQL System
• Authors: Jichuan Zeng, Xi Victoria Lin, Caiming Xiong, Richard Socher, Michael R. Lyu, Irwin King, Steven C.H. Hoi
• Abstract summary: We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a mapping cannot be immediately determined. The proposed method effectively improves the robustness of text-to-native system against untranslatable user input.
• Score: 189.1405317853752
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Natural language interfaces to databases (NLIDB) democratize end user access to relational data. Due to fundamental differences between natural language communication and programming, it is common for end users to issue questions that are ambiguous to the system or fall outside the semantic scope of its underlying query language. We present Photon, a robust, modular, cross-domain NLIDB that can flag natural language input to which a SQL mapping cannot be immediately determined. Photon consists of a strong neural semantic parser (63.2\% structure accuracy on the Spider dev benchmark), a human-in-the-loop question corrector, a SQL executor and a response generator. The question corrector is a discriminative neural sequence editor which detects confusion span(s) in the input question and suggests rephrasing until a translatable input is given by the user or a maximum number of iterations are conducted. Experiments on simulated data show that the proposed method effectively improves the robustness of text-to-SQL system against untranslatable user input. The live demo of our system is available at http://naturalsql.com.

Related papers

• UNITE: A Unified Benchmark for Text-to-SQL Evaluation [72.72040379293718]
  We introduce a UNIfied benchmark for Text-to-domain systems. It is composed of publicly available text-to-domain datasets and 29K databases. Compared to the widely used Spider benchmark, we introduce a threefold increase in SQL patterns.
  arXiv  Detail & Related papers  (2023-05-25T17:19:52Z)
• xDBTagger: Explainable Natural Language Interface to Databases Using Keyword Mappings and Schema Graph [0.17188280334580192]
  Translating natural language queries into structured query language (NLQ) in interfaces to relational databases is a challenging task. We propose xDBTagger, an explainable hybrid translation pipeline that explains the decisions made along the way to the user both textually and visually. xDBTagger is effective in terms of accuracy and translates the queries more efficiently compared to other state-of-the-art pipeline-based systems up to 10000 times.
  arXiv  Detail & Related papers  (2022-10-07T18:17:09Z)
• SUN: Exploring Intrinsic Uncertainties in Text-to-SQL Parsers [61.48159785138462]
  This paper aims to improve the performance of text-to-dependence by exploring the intrinsic uncertainties in the neural network based approaches (called SUN) Extensive experiments on five benchmark datasets demonstrate that our method significantly outperforms competitors and achieves new state-of-the-art results.
  arXiv  Detail & Related papers  (2022-09-14T06:27:51Z)
• A Survey on Text-to-SQL Parsing: Concepts, Methods, and Future Directions [102.8606542189429]
  The goal of text-to-corpora parsing is to convert a natural language (NL) question to its corresponding structured query language () based on the evidences provided by databases. Deep neural networks have significantly advanced this task by neural generation models, which automatically learn a mapping function from an input NL question to an output query.
  arXiv  Detail & Related papers  (2022-08-29T14:24:13Z)
• Speech-to-SQL: Towards Speech-driven SQL Query Generation From Natural Language Question [18.40290951253122]
  Speech-based inputs have been gaining significant momentum with the popularity of smartphones and tablets. This paper works towards designing more effective speech interfaces to query the structured data databases. We propose a novel end-to-end neural architecture named SpeechNet to directly translate human speech into queries.
  arXiv  Detail & Related papers  (2022-01-04T15:38:36Z)
• Weakly Supervised Text-to-SQL Parsing through Question Decomposition [53.22128541030441]
  We take advantage of the recently proposed question meaning representation called QDMR. Given questions, their QDMR structures (annotated by non-experts or automatically predicted) and the answers, we are able to automatically synthesizesql queries. Our results show that the weakly supervised models perform competitively with those trained on NL- benchmark data.
  arXiv  Detail & Related papers  (2021-12-12T20:02:42Z)
• Turing: an Accurate and Interpretable Multi-Hypothesis Cross-Domain Natural Language Database Interface [11.782395912109324]
  Natural language database interface (NLDB) can democratize data-driven insights for non-technical users. This work presents Turing, a NLDB system toward bridging this gap. The cross-domain semantic validation method of Turing achieves $751%$ execution accuracy, and $78.3%$ top-5 beam execution accuracy on the Spider set.
  arXiv  Detail & Related papers  (2021-06-08T17:46:20Z)
• "What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL [49.85635994436742]
  We include human in the loop and present a novel-independent interactive approach (PIIA) that interacts with users using multi-choice questions. PIIA is capable of enhancing the text-to-domain performance with limited interaction turns by using both simulation and human evaluation.
  arXiv  Detail & Related papers  (2020-11-09T02:14:33Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.