AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries

• URL: http://arxiv.org/abs/2406.19073v2
• Date: Thu, 31 Oct 2024 13:59:05 GMT
• Title: AMBROSIA: A Benchmark for Parsing Ambiguous Questions into Database Queries
• Authors: Irina Saparina, Mirella Lapata,
• Abstract summary: We introduce a new benchmark, AMBROSIA, which we hope will inform and inspire the development of text-to-open programs. Our dataset contains questions showcasing three different types of ambiguity (scope ambiguity, attachment ambiguity, and vagueness) In each case, the ambiguity persists even when the database context is provided. This is achieved through a novel approach that involves controlled generation of databases from scratch.
• Score: 56.82807063333088
• License:
• Abstract: Practical semantic parsers are expected to understand user utterances and map them to executable programs, even when these are ambiguous. We introduce a new benchmark, AMBROSIA, which we hope will inform and inspire the development of text-to-SQL parsers capable of recognizing and interpreting ambiguous requests. Our dataset contains questions showcasing three different types of ambiguity (scope ambiguity, attachment ambiguity, and vagueness), their interpretations, and corresponding SQL queries. In each case, the ambiguity persists even when the database context is provided. This is achieved through a novel approach that involves controlled generation of databases from scratch. We benchmark various LLMs on AMBROSIA, revealing that even the most advanced models struggle to identify and interpret ambiguity in questions.

Related papers

• PRACTIQ: A Practical Conversational Text-to-SQL dataset with Ambiguous and Unanswerable Queries [32.40808001281668]
  Real user questions can often be ambiguous with multiple interpretations or unanswerable due to a lack of relevant data. In this work, we construct a practical conversational text-to-text dataset. We generate conversations with four turns: the initial user question, an assistant response seeking clarification, the user's clarification, and the assistant's clarified.
  arXiv  Detail & Related papers  (2024-10-14T20:36:35Z)
• Retrieval-augmented GPT-3.5-based Text-to-SQL Framework with Sample-aware Prompting and Dynamic Revision Chain [21.593701177605652]
  We propose a Text-to-aware prompting framework, involving a sample and a dynamic revision chain. Our approach incorporates sample demonstrations and fine-grained information related to the given question. To generate executable and accuratesqls without human intervention, we design a dynamic revision chain which iteratively adapts fine-grained feedback.
  arXiv  Detail & Related papers  (2023-07-11T07:16:22Z)
• QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing [46.05006486399823]
  This paper presents QURG, a novel Question Rewriting Guided approach to help the models achieve adequate contextual understanding. We first train a question rewriting model to complete the current question based on question context, and convert them into a rewriting edit matrix. We further design a two-stream matrix encoder to jointly model rewriting relations between question and context, and the schema linking relations between natural language and structured schema.
  arXiv  Detail & Related papers  (2023-05-11T08:45:55Z)
• 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)
• XRICL: Cross-lingual Retrieval-Augmented In-Context Learning for Cross-lingual Text-to-SQL Semantic Parsing [70.40401197026925]
  In-context learning using large language models has recently shown surprising results for semantic parsing tasks. This work introduces the XRICL framework, which learns to retrieve relevant English exemplars for a given query. We also include global translation exemplars for a target language to facilitate the translation process for large language models.
  arXiv  Detail & Related papers  (2022-10-25T01:33:49Z)
• 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)
• SPARQLing Database Queries from Intermediate Question Decompositions [7.475027071883912]
  To translate natural language questions into database queries, most approaches rely on a fully annotated training set. We reduce this burden using grounded in databases intermediate question representations. Our pipeline consists of two parts: a semantic that converts natural language questions into the intermediate representations and a non-trainable transpiler to the QLSPAR query language.
  arXiv  Detail & Related papers  (2021-09-13T17:57:12Z)
• "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.