QURG: Question Rewriting Guided Context-Dependent Text-to-SQL Semantic Parsing

Context-dependent Text-to-SQL aims to translate multi-turn natural language questions into SQL queries. Despite various methods have exploited context-dependence information implicitly for contextual SQL parsing, there are few attempts to explicitly address the dependencies between current question and question context. This paper presents QURG, a novel Question Rewriting Guided approach to help the models achieve adequate contextual understanding. Specifically, 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 the rewriting relations between question and context, and the schema linking relations between natural language and structured schema. Experimental results show that QURG significantly improves the performances on two large-scale context-dependent datasets SParC and CoSQL, especially for hard and long-turn questions.

[1]  Li Dong,et al.  GanLM: Encoder-Decoder Pre-training with an Auxiliary Discriminator , 2022, ACL.

[2]  B. Dong,et al.  HIE-SQL: History Information Enhanced Network for Context-Dependent Text-to-SQL Semantic Parsing , 2022, FINDINGS.

[3]  Yutian Li,et al.  Pay More Attention to History: A Context Modeling Strategy for Conversational Text-to-SQL , 2021, INTERSPEECH.

[4]  Dzmitry Bahdanau,et al.  PICARD: Parsing Incrementally for Constrained Auto-Regressive Decoding from Language Models , 2021, EMNLP.

[5]  Gangwoo Kim,et al.  Learn to Resolve Conversational Dependency: A Consistency Training Framework for Conversational Question Answering , 2021, ACL.

[6]  Kai Yu,et al.  Decoupled Dialogue Modeling and Semantic Parsing for Multi-Turn Text-to-SQL , 2021, FINDINGS.

[7]  Kai Yu,et al.  LGESQL: Line Graph Enhanced Text-to-SQL Model with Mixed Local and Non-Local Relations , 2021, ACL.

[8]  Pengfei Zhu,et al.  Dynamic Hybrid Relation Exploration Network for Cross-Domain Context-Dependent Semantic Parsing , 2021, AAAI.

[9]  Xiaodong He,et al.  Conversational Query Rewriting with Self-Supervised Learning , 2021, ICASSP 2021 - 2021 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP).

[10]  Yu Hu,et al.  Tracking Interaction States for Multi-Turn Text-to-SQL Semantic Parsing , 2020, AAAI.

[11]  Richard Socher,et al.  Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing , 2020, FINDINGS.

[12]  Xiaojun Wan,et al.  IGSQL: Database Schema Interaction Graph Based Neural Model for Context-Dependent Text-to-SQL Generation , 2020, EMNLP.

[13]  Dzmitry Bahdanau,et al.  DuoRAT: Towards Simpler Text-to-SQL Models , 2020, NAACL.

[14]  Zhucheng Tu,et al.  Open-Domain Question Answering Goes Conversational via Question Rewriting , 2020, NAACL.

[15]  Dragomir R. Radev,et al.  GraPPa: Grammar-Augmented Pre-Training for Table Semantic Parsing , 2020, ICLR.

[16]  Dongmei Zhang,et al.  Incomplete Utterance Rewriting as Semantic Segmentation , 2020, EMNLP.

[17]  Sida I. Wang,et al.  Grounded Adaptation for Zero-shot Executable Semantic Parsing , 2020, EMNLP.

[18]  Jamie Callan,et al.  CAsT-19: A Dataset for Conversational Information Seeking , 2020, SIGIR.

[19]  Paul N. Bennett,et al.  Few-Shot Generative Conversational Query Rewriting , 2020, SIGIR.

[20]  S. Longpre,et al.  Question Rewriting for Conversational Question Answering , 2020, WSDM.

[21]  Jimmy J. Lin,et al.  Conversational Question Reformulation via Sequence-to-Sequence Architectures and Pretrained Language Models , 2020, ArXiv.

[22]  Shuangzhi Wu,et al.  Alternating Language Modeling for Cross-Lingual Pre-Training , 2020, AAAI.

[23]  Quoc V. Le,et al.  ELECTRA: Pre-training Text Encoders as Discriminators Rather Than Generators , 2020, ICLR.

[24]  Bin Zhou,et al.  How Far are We from Effective Context Modeling ? An Exploratory Study on Semantic Parsing in Context , 2020, IJCAI.

[25]  Xiaodong Liu,et al.  RAT-SQL: Relation-Aware Schema Encoding and Linking for Text-to-SQL Parsers , 2019, ACL.

[26]  Jordan Boyd-Graber,et al.  Can You Unpack That? Learning to Rewrite Questions-in-Context , 2019, EMNLP.

[27]  Yan Wang,et al.  Improving Open-Domain Dialogue Systems via Multi-Turn Incomplete Utterance Restoration , 2019, EMNLP.

[28]  Omer Levy,et al.  BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and Comprehension , 2019, ACL.

[29]  Colin Raffel,et al.  Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer , 2019, J. Mach. Learn. Res..

[30]  Zhoujun Li,et al.  Low-Resource Response Generation with Template Prior , 2019, EMNLP.

[31]  Luyao Chen,et al.  CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases , 2019, EMNLP.

[32]  Tao Yu,et al.  Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions , 2019, EMNLP.

[33]  Cheng Niu,et al.  Improving Multi-turn Dialogue Modelling with Utterance ReWriter , 2019, ACL.

[34]  Tao Yu,et al.  SParC: Cross-Domain Semantic Parsing in Context , 2019, ACL.

[35]  Graham Neubig,et al.  TRANX: A Transition-based Neural Abstract Syntax Parser for Semantic Parsing and Code Generation , 2018, EMNLP.

[36]  Tao Yu,et al.  Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task , 2018, EMNLP.

[37]  Eunsol Choi,et al.  QuAC: Question Answering in Context , 2018, EMNLP.

[38]  Ashish Vaswani,et al.  Self-Attention with Relative Position Representations , 2018, NAACL.

[39]  Richard Socher,et al.  Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning , 2018, ArXiv.

[40]  Lukasz Kaiser,et al.  Attention is All you Need , 2017, NIPS.

[41]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[42]  Jian Yang,et al.  KINet: Incorporating Relevant Facts Into Knowledge-Grounded Dialog Generation , 2023, IEEE/ACM Transactions on Audio, Speech, and Language Processing.

[43]  Alex Polozov,et al.  SCoRe: Pre-Training for Context Representation in Conversational Semantic Parsing , 2021, ICLR.

[44]  Zhaopeng Tu,et al.  RAST: Domain-Robust Dialogue Rewriting as Sequence Tagging , 2021, EMNLP.

[45]  Danqi Chen,et al.  of the Association for Computational Linguistics: , 2001 .