Mention Extraction and Linking for SQL Query Generation

• URL: http://arxiv.org/abs/2012.10074v1
• Date: Fri, 18 Dec 2020 06:51:23 GMT
• Title: Mention Extraction and Linking for SQL Query Generation
• Authors: Jianqiang Ma, Zeyu Yan, Shuai Pang, Yang Zhang, Jianping Shen
• Abstract summary: On the Wiki benchmark, state-of-the-art text-to-text systems typically take a slot-filling approach by building several dedicated models for each type of slots. This paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence. Trained with automatically generated annotations, the proposed method achieves the first place on the Wiki benchmark.
• Score: 6.186311061181687
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: On the WikiSQL benchmark, state-of-the-art text-to-SQL systems typically take a slot-filling approach by building several dedicated models for each type of slots. Such modularized systems are not only complex butalso of limited capacity for capturing inter-dependencies among SQL clauses. To solve these problems, this paper proposes a novel extraction-linking approach, where a unified extractor recognizes all types of slot mentions appearing in the question sentence before a linker maps the recognized columns to the table schema to generate executable SQL queries. Trained with automatically generated annotations, the proposed method achieves the first place on the WikiSQL benchmark.

Related papers

• RSL-SQL: Robust Schema Linking in Text-to-SQL Generation [12.765849111313614]
  We propose a novel framework called RSL- that combines bidirectional schema linking, contextual information augmentation, binary selection strategy, and multi-turn self-correction. Experiments on the BIRD and Spider benchmarks demonstrate that our approach achieves state-of-the-art execution accuracy among open-source solutions.
  arXiv  Detail & Related papers  (2024-10-31T16:22:26Z)
• MAG-SQL: Multi-Agent Generative Approach with Soft Schema Linking and Iterative Sub-SQL Refinement for Text-to-SQL [15.824894030016187]
  Recent In-Context Learning based methods have achieved remarkable success in Text-to-Context task. There is still a large gap between the performance of these models and human performance on datasets with complex database schema and difficult questions, such as. In our framework, an entity-based method with tables' summary is used to select the columns in database, and a novel targets-conditions decomposition method is introduced to decompose those complex questions.
  arXiv  Detail & Related papers  (2024-08-15T04:57:55Z)
• The Death of Schema Linking? Text-to-SQL in the Age of Well-Reasoned Language Models [0.9149661171430259]
  We revisit schema linking when using the latest generation of large language models (LLMs) We find empirically that newer models are adept at utilizing relevant schema elements during generation even in the presence of large numbers of irrelevant ones. Instead of filtering contextual information, we highlight techniques such as augmentation, selection, and correction, and adopt them to improve the accuracy of our Text-to-BIRD pipeline.
  arXiv  Detail & Related papers  (2024-08-14T17:59:04Z)
• SQL-to-Schema Enhances Schema Linking in Text-to-SQL [15.6857201570992]
  In text-to-speech methods, there is a need to filter out unnecessary tables and columns. Previous approaches have involved sorting tables and columns based on their relevance to the question. We propose an inventive schema linking method in two steps.
  arXiv  Detail & Related papers  (2024-05-15T12:22:48Z)
• Schema-Aware Multi-Task Learning for Complex Text-to-SQL [4.913409359995421]
  We present a schema-aware multi-task learning framework (named MT) for complicatedsql queries. Specifically, we design a schema linking discriminator module to distinguish the valid question-schema linkings. On the decoder side, we define 6-type relationships to describe the connections between tables and columns.
  arXiv  Detail & Related papers  (2024-03-09T01:13:37Z)
• SQLPrompt: In-Context Text-to-SQL with Minimal Labeled Data [54.69489315952524]
  "Prompt" is designed to improve the few-shot prompting capabilities of Text-to-LLMs. "Prompt" outperforms previous approaches for in-context learning with few labeled data by a large margin. We show that emphPrompt outperforms previous approaches for in-context learning with few labeled data by a large margin.
  arXiv  Detail & Related papers  (2023-11-06T05:24:06Z)
• Improving Text-to-SQL Semantic Parsing with Fine-grained Query Understanding [84.04706075621013]
  We present a general-purpose, modular neural semantic parsing framework based on token-level fine-grained query understanding. Our framework consists of three modules: named entity recognizer (NER), neural entity linker (NEL) and neural entity linker (NSP)
  arXiv  Detail & Related papers  (2022-09-28T21:00:30Z)
• Proton: Probing Schema Linking Information from Pre-trained Language Models for Text-to-SQL Parsing [66.55478402233399]
  We propose a framework to elicit relational structures via a probing procedure based on Poincar'e distance metric. Compared with commonly-used rule-based methods for schema linking, we found that probing relations can robustly capture semantic correspondences. Our framework sets new state-of-the-art performance on three benchmarks.
  arXiv  Detail & Related papers  (2022-06-28T14:05:25Z)
• S$^2$SQL: Injecting Syntax to Question-Schema Interaction Graph Encoder for Text-to-SQL Parsers [66.78665327694625]
  We propose S$2$, injecting Syntax to question- encoder graph for Text-to- relational parsing. We also employ the decoupling constraint to induce diverse edge embedding, which further improves the network's performance. Experiments on the Spider and robustness setting Spider-Syn demonstrate that the proposed approach outperforms all existing methods when pre-training models are used.
  arXiv  Detail & Related papers  (2022-03-14T09:49:15Z)
• Dual Reader-Parser on Hybrid Textual and Tabular Evidence for Open Domain Question Answering [78.9863753810787]
  A large amount of world's knowledge is stored in structured databases. query languages can answer questions that require complex reasoning, as well as offering full explainability.
  arXiv  Detail & Related papers  (2021-08-05T22:04:13Z)
• Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing [110.97778888305506]
  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. BRIDGE attained state-of-the-art performance on popular cross-DB text-to- relational benchmarks. Our analysis shows that BRIDGE effectively captures the desired cross-modal dependencies and has the potential to generalize to more text-DB related tasks.
  arXiv  Detail & Related papers  (2020-12-23T12:33:52Z)

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.