"What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL

• URL: http://arxiv.org/abs/2011.04151v1
• Date: Mon, 9 Nov 2020 02:14:33 GMT
• Title: "What Do You Mean by That?" A Parser-Independent Interactive Approach for Enhancing Text-to-SQL
• Authors: Yuntao Li, Bei Chen, Qian Liu, Yan Gao, Jian-Guang Lou, Yan Zhang, Dongmei Zhang
• Abstract summary: 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.
• Score: 49.85635994436742
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In Natural Language Interfaces to Databases systems, the text-to-SQL technique allows users to query databases by using natural language questions. Though significant progress in this area has been made recently, most parsers may fall short when they are deployed in real systems. One main reason stems from the difficulty of fully understanding the users' natural language questions. In this paper, we include human in the loop and present a novel parser-independent interactive approach (PIIA) that interacts with users using multi-choice questions and can easily work with arbitrary parsers. Experiments were conducted on two cross-domain datasets, the WikiSQL and the more complex Spider, with five state-of-the-art parsers. These demonstrated that PIIA is capable of enhancing the text-to-SQL performance with limited interaction turns by using both simulation and human evaluation.

Related papers

• SQLucid: Grounding Natural Language Database Queries with Interactive Explanations [28.10727203675818]
  SQLucid is a novel user interface that bridges the gap between non-expert users and complex database querying processes. Our code is available at https://github.com/magic-YuanTian/ucid.
  arXiv  Detail & Related papers  (2024-09-10T03:14:09Z)
• SQL-PaLM: Improved Large Language Model Adaptation for Text-to-SQL (extended) [53.95151604061761]
  This paper introduces the framework for enhancing Text-to- filtering using large language models (LLMs) With few-shot prompting, we explore the effectiveness of consistency decoding with execution-based error analyses. With instruction fine-tuning, we delve deep in understanding the critical paradigms that influence the performance of tuned LLMs.
  arXiv  Detail & Related papers  (2023-05-26T21:39:05Z)
• Interactive Text-to-SQL Generation via Editable Step-by-Step Explanations [31.3376894001311]
  We introduce a new interaction mechanism that allows users to directly edit a step-by-step explanation of a query to fix errors. Our experiments on multiple datasets, as well as a user with 24 participants, demonstrate that our approach can achieve better than multiple SOTA approaches.
  arXiv  Detail & Related papers  (2023-05-12T10:45:29Z)
• Towards Generalizable and Robust Text-to-SQL Parsing [77.18724939989647]
  We propose a novel TKK framework consisting of Task decomposition, Knowledge acquisition, and Knowledge composition to learn text-to- parsing in stages. We show that our framework is effective in all scenarios and state-of-the-art performance on the Spider, SParC, and Co. datasets.
  arXiv  Detail & Related papers  (2022-10-23T09:21:27Z)
• 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)
• 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)
• Photon: A Robust Cross-Domain Text-to-SQL System [189.1405317853752]
  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.
  arXiv  Detail & Related papers  (2020-07-30T07:44:48Z)

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.