DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks

• URL: http://arxiv.org/abs/2101.04226v1
• Date: Mon, 11 Jan 2021 22:54:39 GMT
• Title: DBTagger: Multi-Task Learning for Keyword Mapping in NLIDBs Using Bi-Directional Recurrent Neural Networks
• Authors: Arif Usta, Akifhan Karakayali and \"Ozg\"ur Ulusoy
• Abstract summary: We propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, $92.4%$ on the average.
• Score: 0.2578242050187029
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Translating Natural Language Queries (NLQs) to Structured Query Language (SQL) in interfaces deployed in relational databases is a challenging task, which has been widely studied in database community recently. Conventional rule based systems utilize series of solutions as a pipeline to deal with each step of this task, namely stop word filtering, tokenization, stemming/lemmatization, parsing, tagging, and translation. Recent works have mostly focused on the translation step overlooking the earlier steps by using ad-hoc solutions. In the pipeline, one of the most critical and challenging problems is keyword mapping; constructing a mapping between tokens in the query and relational database elements (tables, attributes, values, etc.). We define the keyword mapping problem as a sequence tagging problem, and propose a novel deep learning based supervised approach that utilizes POS tags of NLQs. Our proposed approach, called \textit{DBTagger} (DataBase Tagger), is an end-to-end and schema independent solution, which makes it practical for various relational databases. We evaluate our approach on eight different datasets, and report new state-of-the-art accuracy results, $92.4\%$ on the average. Our results also indicate that DBTagger is faster than its counterparts up to $10000$ times and scalable for bigger databases.

Related papers

• E-SQL: Direct Schema Linking via Question Enrichment in Text-to-SQL [1.187832944550453]
  We introduce E- repository, a novel pipeline designed to address challenges through direct schema linking and candidate predicate augmentation. E- enhances the natural language query by incorporating relevant database items (i.e. tables, columns, and values) and conditions directly into the question, bridging the gap between the query and the database structure. We investigate the impact of schema filtering, a technique widely explored in previous work, and demonstrate its diminishing returns when applied alongside advanced large language models.
  arXiv  Detail & Related papers  (2024-09-25T09:02:48Z)
• Improving Retrieval-augmented Text-to-SQL with AST-based Ranking and Schema Pruning [10.731045939849125]
  We focus on Text-to- semantic parsing from the perspective of retrieval-augmented generation. Motivated by challenges related to the size of commercial database schemata and the deployability of business intelligence solutions, we propose $textASTReS$ that dynamically retrieves input database information.
  arXiv  Detail & Related papers  (2024-07-03T15:55:14Z)
• CHESS: Contextual Harnessing for Efficient SQL Synthesis [1.9506402593665235]
  We propose a new pipeline that retrieves relevant data and context, selects an efficient schema, and synthesizes correct and efficient queries. Our method achieves new state-of-the-art performance on the cross-domain challenging BIRD dataset.
  arXiv  Detail & Related papers  (2024-05-27T01:54:16Z)
• 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)
• Semantic Parsing for Conversational Question Answering over Knowledge Graphs [63.939700311269156]
  We develop a dataset where user questions are annotated with Sparql parses and system answers correspond to execution results thereof. We present two different semantic parsing approaches and highlight the challenges of the task. Our dataset and models are released at https://github.com/Edinburgh/SPICE.
  arXiv  Detail & Related papers  (2023-01-28T14:45:11Z)
• Uni-Parser: Unified Semantic Parser for Question Answering on Knowledge Base and Database [86.03294330305097]
  We propose a unified semantic element for question answering (QA) on both knowledge bases (KB) and databases (DB) We introduce the primitive (relation and entity in KB, table name, column name and cell value in DB) as an essential element in our framework. We leverage the generator to predict final logical forms by altering and composing topranked primitives with different operations.
  arXiv  Detail & Related papers  (2022-11-09T19:33:27Z)
• STAR: SQL Guided Pre-Training for Context-dependent Text-to-SQL Parsing [64.80483736666123]
  We propose a novel pre-training framework STAR for context-dependent text-to- parsing. In addition, we construct a large-scale context-dependent text-to-the-art conversation corpus to pre-train STAR. Extensive experiments show that STAR achieves new state-of-the-art performance on two downstream benchmarks.
  arXiv  Detail & Related papers  (2022-10-21T11:30:07Z)
• 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)
• Multilingual Autoregressive Entity Linking [49.35994386221958]
  mGENRE is a sequence-to-sequence system for the Multilingual Entity Linking problem. For a mention in a given language, mGENRE predicts the name of the target entity left-to-right, token-by-token. We show the efficacy of our approach through extensive evaluation including experiments on three popular MEL benchmarks.
  arXiv  Detail & Related papers  (2021-03-23T13:25:55Z)
• 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.