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The task of multi-turn text-to-SQL semantic parsing aims to translate natural language utterances in an interaction into SQL queries in order to answer them using a database which normally contains multiple table schemas.
SParC: Cross-Domain Semantic Parsing in Context
Yu, T.; Zhang, R.; Yasunaga, M.; Tan, Y. C.; Lin, X. V.; Li, S.; Er, H.; Li, I.; Pang, B.; Chen, T.; et al. 2019b · 1906
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CoSQL: A Conversational Text-to-SQL Challenge Towards Cross-Domain Natural Language Interfaces to Databases
Yu, T.; Zhang, R.; Er, H.; Li, S.; Xue, E.; Pang, B.; Lin, X. V.; Tan, Y. C.; Shi, T.; Li, Z.; et al. 2019a · 1979
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The ATIS spoken language systems pilot corpus
Hemphill, C. T.; Godfrey, J. J.; and Doddington, G. R. 1990 · 1990
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Expanding the scope of the ATIS task: The ATIS-3 corpus
Dahl, D. A.; Bates, M.; Brown, M.; Fisher, W.; Hunicke-Smith, K.; Pallett, D.; Pao, C.; Rudnicky, A.; and Shriberg, E. 1994 · 1994
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A fully statistical approach to natural language interfaces
Miller, S.; Stallard, D.; Bobrow, R.; and Schwartz, R. 1996 · 1996
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Learning to parse database queries using inductive logic programming
Zelle, J. M.; and Mooney, R. J. 1996 · 1996
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Learning to map sentences to logical form: structured classification with probabilistic categorial grammars
Zettlemoyer, L. S.; and Collins, M. 2005 · 2005
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Driving semantic parsing from the world’s response
Clarke, J.; Goldwasser, D.; Chang, M.-W.; and Roth, D. 2010 · 2010
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Bootstrapping semantic parsers from conversations
Artzi, Y.; and Zettlemoyer, L. 2011 · 2011
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Seq2SQL: Generating Structured Queries from Natural Language using Reinforcement Learning
Zhong, V.; Xiong, C.; and Socher, R. 2017 · 2012
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The Second Dialog State Tracking Challenge
Henderson, M.; Thomson, B.; and Williams, J. D. 2014 · 2014
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Adam: A method for stochastic optimization
Kingma, D. P.; and Ba, J. 2014 · 2014
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Building a semantic parser overnight
Wang, Y.; Berant, J.; and Liang, P. 2015 · 2015
Cited alongside, same era.
Language to Logical Form with Neural Attention
Dong, L.; and Lapata, M. 2016 · 2016
Cited alongside, same era.
Search-based neural structured learning for sequential question answering
Iyyer, M.; Yih, W.-t.; and Chang, M.-W. 2017 · 2017
Cited alongside, same era.
Neural semantic parsing with type constraints for semi-structured tables
Krishnamurthy, J.; Dasigi, P.; and Gardner, M. 2017 · 2017
Cited alongside, same era.
Neural Belief Tracker: Data-Driven Dialogue State Tracking
Mrksic, N.; Séaghdha, D. Ó.; Wen, T.; Thomson, B.; and Young, S. J. 2017 · 2017
Cited alongside, same era.
Automatic differentiation in pytorch
Paszke, A.; Gross, S.; Chintala, S.; Chanan, G.; Yang, E.; DeVito, Z.; Lin, Z.; Desmaison, A.; Antiga, L.; and Lerer, A. 2017 · 2017
Cited alongside, same era.
Learning to Map Context-Dependent Sentences to Executable Formal Queries
Suhr, A.; Iyer, S.; and Artzi, Y. 2018 · 2018
Later among the works it cites.
SyntaxSQLNet: Syntax Tree Networks for Complex and Cross-Domain Text-to-SQL Task
Yu, T.; Yasunaga, M.; Yang, K.; Zhang, R.; Wang, D.; Li, Z.; and Radev, D. R. 2018b · 2018
Later among the works it cites.
Spider: A Large-Scale Human-Labeled Dataset for Complex and Cross-Domain Semantic Parsing and Text-to-SQL Task
Yu, T.; Zhang, R.; Yang, K.; Yasunaga, M.; Wang, D.; Li, Z.; Ma, J.; Li, I.; Yao, Q.; Roman, S.; et al. 2018c · 2018
Later among the works it cites.
Global Reasoning over Database Structures for Text-to-SQL Parsing
Bogin, B.; Gardner, M.; and Berant, J. 2019a · 2019
Later among the works it cites.
Interactive matching network for multi-turn response selection in retrieval-based chatbots
Gu, J.-C.; Ling, Z.-H.; and Liu, Q. 2019 · 2019
Later among the works it cites.
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A Network-based End-to-End Trainable Task-oriented Dialogue System
Wen, T.; Vandyke, D.; Mrksic, N.; Gasic, M.; Rojas-Barahona, L. M.; Su, P.; Ultes, S.; and Young, S. J. 2017 · 2017
Cited alongside, same era.
Sqlnet: Generating structured queries from natural language without reinforcement learning
Xu, X.; Liu, C.; and Song, D. 2017 · 2017
Cited alongside, same era.
A Syntactic Neural Model for General-Purpose Code Generation
Yin, P.; and Neubig, G. 2017 · 2017
Cited alongside, same era.
MultiWOZ - A Large-Scale Multi-Domain Wizard-of-Oz Dataset for Task-Oriented Dialogue Modelling
Budzianowski, P.; Wen, T.; Tseng, B.; Casanueva, I.; Ultes, S.; Ramadan, O.; and Gasic, M. 2018 · 2018
Cited alongside, same era.
Coarse-to-Fine Decoding for Neural Semantic Parsing
Dong, L.; and Lapata, M. 2018 · 2018
Cited alongside, same era.
Improving Text-to-SQL Evaluation Methodology
Finegan-Dollak, C.; Kummerfeld, J. K.; Zhang, L.; Ramanathan, K.; Sadasivam, S.; Zhang, R.; and Radev, D. 2018 · 2018
Cited alongside, same era.
Dually Interactive Matching Network for Personalized Response Selection in Retrieval-Based Chatbots
Gu, J.-C.; Ling, Z.-H.; Zhu, X.; and Liu, Q. 2019 · 2019
Later among the works it cites.
Towards Complex Text-to-SQL in Cross-Domain Database with Intermediate Representation
Guo, J.; Zhan, Z.; Gao, Y.; Xiao, Y.; Lou, J.-G.; Liu, T.; and Zhang, D. 2019 · 2019
Later among the works it cites.
Editing-Based SQL Query Generation for Cross-Domain Context-Dependent Questions
Zhang, R.; Yu, T.; Er, H.; Shim, S.; Xue, E.; Lin, X. V.; Shi, T.; Xiong, C.; Socher, R.; and Radev, D. 2019 · 2019
Later among the works it cites.
SAS: Dialogue State Tracking via Slot Attention and Slot Information Sharing
Hu, J.; Yang, Y.; Chen, C.; He, L.; and Yu, Z. 2020 · 2020
Closest in time.
Dialogue State Tracking with Explicit Slot Connection Modeling
Ouyang, Y.; Chen, M.; Dai, X.; Zhao, Y.; Huang, S.; and Chen, J. 2020 · 2020
Closest in time.
Multi-Domain Dialogue Acts and Response Co-Generation
Wang, K.; Tian, J.; Wang, R.; Quan, X.; and Yu, J. 2020 · 2020
Closest in time.
Joint Intent Detection and Entity Linking on Spatial Domain Queries
Zhang, L.; Wang, R.; Zhou, J.; Yu, J.; Ling, Z.; and Xiong, H. 2020 · 2020
Closest in time.