MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL

• URL: http://arxiv.org/abs/2402.10666v5
• Date: Wed, 18 Sep 2024 02:48:25 GMT
• Title: MURRE: Multi-Hop Table Retrieval with Removal for Open-Domain Text-to-SQL
• Authors: Xuanliang Zhang, Dingzirui Wang, Longxu Dou, Qingfu Zhu, Wanxiang Che,
• Abstract summary: Multi-hop table retrieval with removal (MURRE) removes previously retrieved information from the question to guide towards unretrieved relevant tables. Experiments on two open-domain text-to- retriever datasets demonstrate an average improvement of 5.7% over the previous state-of-the-art results.
• Score: 51.48239006107272
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: The open-domain text-to-SQL task aims to retrieve question-relevant tables from massive databases and generate SQL. However, the performance of current methods is constrained by single-hop retrieval, and existing multi-hop retrieval of open-domain question answering is not directly applicable due to the tendency to retrieve tables similar to the retrieved ones but irrelevant to the question. Since the questions in text-to-SQL usually contain all required information, while previous multi-hop retrieval supplements the questions with retrieved documents. Therefore, we propose the multi-hop table retrieval with removal (MURRE), which removes previously retrieved information from the question to guide the retriever towards unretrieved relevant tables. Our experiments on two open-domain text-to-SQL datasets demonstrate an average improvement of 5.7% over the previous state-of-the-art results.

Related papers

• Database-Augmented Query Representation for Information Retrieval [59.57065228857247]
  We present a novel retrieval framework called Database-Augmented Query representation (DAQu) DAQu augments the original query with various (query-related) metadata across multiple tables. We validate DAQu in diverse retrieval scenarios that can incorporate metadata from the relational database.
  arXiv  Detail & Related papers  (2024-06-23T05:02:21Z)
• Is Table Retrieval a Solved Problem? Exploring Join-Aware Multi-Table Retrieval [52.592071689901196]
  We introduce a method that uncovers useful join relations for any query and database during table retrieval. Our method outperforms the state-of-the-art approaches for table retrieval by up to 9.3% in F1 score and for end-to-end QA by up to 5.4% in accuracy.
  arXiv  Detail & Related papers  (2024-04-15T15:55:01Z)
• Denoising Table-Text Retrieval for Open-Domain Question Answering [6.711626456283439]
  In table-text open-domain question answering, a retriever system retrieves relevant evidence from tables and text to answer questions. Previous studies have two common challenges: firstly, their retrievers can be affected by false-positive labels in training datasets. We propose Denoised Table-Text Retriever (DoTTeR) to overcome these issues.
  arXiv  Detail & Related papers  (2024-03-26T11:44:49Z)
• Enhancing Open-Domain Table Question Answering via Syntax- and Structure-aware Dense Retrieval [21.585255812861632]
  Open-domain table question answering aims to provide answers to a question by retrieving and extracting information from a large collection of tables. Existing studies of open-domain table QA either directly adopt text retrieval methods or consider the table structure only in the encoding layer for table retrieval. We propose a syntax- and structure-aware retrieval method for the open-domain table QA task.
  arXiv  Detail & Related papers  (2023-09-19T10:40:09Z)
• 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)
• Open Question Answering over Tables and Text [55.8412170633547]
  In open question answering (QA), the answer to a question is produced by retrieving and then analyzing documents that might contain answers to the question. Most open QA systems have considered only retrieving information from unstructured text. We present a new large-scale dataset Open Table-and-Text Question Answering (OTT-QA) to evaluate performance on this task.
  arXiv  Detail & Related papers  (2020-10-20T16:48:14Z)
• Answering Any-hop Open-domain Questions with Iterative Document Reranking [62.76025579681472]
  We propose a unified QA framework to answer any-hop open-domain questions. Our method consistently achieves performance comparable to or better than the state-of-the-art on both single-hop and multi-hop open-domain QA datasets.
  arXiv  Detail & Related papers  (2020-09-16T04:31:38Z)

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.