Department of Computer Science

Machine Learning Research Group

University of Texas at Austin Artificial Intelligence Lab

Publications: Learning for Semantic Parsing

Semantic parsing is the process of mapping a natural-language sentence into a formal representation of its meaning. A shallow form of semantic representation is a case-role analysis (a.k.a. a semantic role labeling), which identifies roles such as agent, patient, source, and destination. A deeper semantic analysis provides a representation of the sentence in predicate logic or other formal language which supports automated reasoning. We have developed methods for automatically learning semantic parsers from annotated corpora using inductive logic programming and other learning methods. We have explored learning semantic parsers for mapping natural-language sentences to case-role analyses, formal database queries, and formal command languages (i.e. the Robocup coaching language for use in advice-taking learners). We have also explored methods for learning semantic lexicons, i.e. databases of words or phrases paired with one or more alternative formal meaning representations. Semantic lexicons can also be learned from semantically annotated sentences and are an important source of knowledge for semantic parsing. Learning for semantic parsing is part of our research on natural language learning.

"The fish trap exists because of the fish. Once you've gotten the fish you can forget the trap. The rabbit snare exists because of the rabbit. Once you've gotten the rabbit, you can forget the snare. Words exist because of meaning. Once you've gotten the meaning, you can forget the words. Where can I find a man who has forgotten words so I can talk with him?"
-- The Writings of Chuang Tzu, 4th century B.C. (Original text in Chinese)

Demos of learned natural-language database interfaces:

Tutorial on semantic parsing presented at ACL 2010:


Show abstracts
  1. Multimodal Contextualized Semantic Parsing from Speech
    [Details] [PDF] [Slides (PDF)] [Poster] [Video]
    Jordan Voas, Raymond Mooney, David Harwath
    In Association for Computational Linguistics (ACL), August 2024.
  2. When is Tree Search Useful for LLM Planning? It Depends on the Discriminator
    [Details] [PDF] [Slides (PDF)] [Poster] [Video]
    Ziru Chen, Michael White, Raymond Mooney, Ali Payani, Yu Su, Huan Sun
    In Association for Computational Linguistics (ACL), August 2024.
  3. Using Planning to Improve Semantic Parsing of Instructional Texts
    [Details] [PDF] [Slides (PDF)]
    Vanya Cohen, Raymond Mooney
    Association of Computational Linguistics (ACL), Natural Language Reasoning and Structured Explanations Workshop, July 2023.
  4. Text-to-SQL Error Correction with Language Models of Code
    [Details] [PDF] [Poster]
    Ziru Chen, Shijie Chen, Michael White, Raymond Mooney, Ali Payani, Jayanth Srinivasa, Yu Su, Huan Sun
    In Proceedings of the Association for Computational Linguistics (ACL), January 2023.
  5. Dialog as a Vehicle for Lifelong Learning of Grounded Language Understanding Systems
    [Details] [PDF] [Slides (PDF)]
    Aishwarya Padmakumar
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, August 2020.
  6. Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, Raymond J. Mooney
    The Journal of Artificial Intelligence Research (JAIR), 67:327-374, February 2020.
  7. Improving Grounded Natural Language Understanding through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney
    In IEEE International Conference on Robotics and Automation (ICRA), Montreal, Canada, May 2019.
  8. Improved Models and Queries for Grounded Human-Robot Dialog
    [Details] [PDF]
    Aishwarya Padmakumar
    October 2018. PhD Proposal, Department of Computer Science, The University of Texas At Austin.
  9. Interaction and Autonomy in RoboCup@Home and Building-Wide Intelligence
    [Details] [PDF]
    Justin Hart, Harel Yedidsion, Yuqian Jiang, Nick Walker, Rishi Shah, Jesse Thomason, Aishwarya Padmakumar, Rolando Fernandez, Jivko Sinapov, Raymond Mooney, Peter Stone
    In Artificial Intelligence (AI) for Human-Robot Interaction (HRI) symposium, AAAI Fall Symposium Series, Arlington, Virginia, October 2018.
  10. Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney
    In Late-breaking Track at the SIGDIAL Special Session on Physically Situated Dialogue (RoboDIAL-18), Melbourne, Australia, July 2018.
  11. Jointly Improving Parsing and Perception for Natural Language Commands through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason, Aishwarya Padmakumar, Jivko Sinapov, Nick Walker, Yuqian Jiang, Harel Yedidsion, Justin Hart, Peter Stone, and Raymond J. Mooney
    In RSS Workshop on Models and Representations for Natural Human-Robot Communication (MRHRC-18). Robotics: Science and Systems (RSS), June 2018.
  12. Continually Improving Grounded Natural Language Understanding through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason
    PhD Thesis, Department of Computer Science, The University of Texas at Austin, April 2018.
  13. Improving Black-box Speech Recognition using Semantic Parsing
    [Details] [PDF] [Poster]
    Rodolfo Corona and Jesse Thomason and Raymond J. Mooney
    In Proceedings of the 8th International Joint Conference on Natural Language Processing (IJCNLP-17), 122-127, Taipei, Taiwan, November 2017.
  14. Dialog for Natural Language to Code
    [Details] [PDF]
    Shobhit Chaurasia
    2017. Masters Thesis, Computer Science Department, University of Texas at Austin.
  15. Integrated Learning of Dialog Strategies and Semantic Parsing
    [Details] [PDF]
    Aishwarya Padmakumar and Jesse Thomason and Raymond J. Mooney
    In Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics (EACL 2017), 547--557, Valencia, Spain, April 2017.
  16. An Analysis of Using Semantic Parsing for Speech Recognition
    [Details] [PDF] [Slides (PPT)]
    Rodolfo Corona
    2016. Undergraduate Honors Thesis, Computer Science Department, University of Texas at Austin.
  17. Continuously Improving Natural Language Understanding for Robotic Systems through Semantic Parsing, Dialog, and Multi-modal Perception
    [Details] [PDF]
    Jesse Thomason
    November 2016. PhD proposal, Department of Computer Science, The University of Texas at Austin.
  18. Improved Semantic Parsers For If-Then Statements
    [Details] [PDF]
    I. Beltagy and Chris Quirk
    To Appear In Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (ACL-16), Berlin, Germany, 2016.
  19. Language to Code: Learning Semantic Parsers for If-This-Then-That Recipes
    [Details] [PDF] [Poster]
    Chris Quirk and Raymond Mooney and Michel Galley
    In Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics (ACL-15), 878--888, Beijing, China, July 2015.
  20. Learning to Interpret Natural Language Commands through Human-Robot Dialog
    [Details] [PDF]
    Jesse Thomason and Shiqi Zhang and Raymond Mooney and Peter Stone
    In Proceedings of the 2015 International Joint Conference on Artificial Intelligence (IJCAI), 1923--1929, Buenos Aires, Argentina, July 2015.
  21. Semantic Parsing using Distributional Semantics and Probabilistic Logic
    [Details] [PDF] [Poster]
    I. Beltagy and Katrin Erk and Raymond Mooney
    In Proceedings of ACL 2014 Workshop on Semantic Parsing (SP-2014), 7--11, Baltimore, MD, June 2014.
  22. Grounded Language Learning Models for Ambiguous Supervision
    [Details] [PDF] [Slides (PPT)]
    Joo Hyun Kim
    PhD Thesis, Department of Computer Science, University of Texas at Austin, December 2013.
  23. Adapting Discriminative Reranking to Grounded Language Learning
    [Details] [PDF] [Slides (PPT)]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 51st Annual Meeting of the Association for Computational Linguistics (ACL-2013), 218--227, Sofia, Bulgaria, August 2013.
  24. Generative Models of Grounded Language Learning with Ambiguous Supervision
    [Details] [PDF] [Slides (PPT)]
    Joohyun Kim
    Technical Report, PhD proposal, Department of Computer Science, The University of Texas at Austin, June 2012.
  25. Unsupervised PCFG Induction for Grounded Language Learning with Highly Ambiguous Supervision
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the Conference on Empirical Methods in Natural Language Processing and Natural Language Learning (EMNLP-CoNLL '12), 433--444, Jeju Island, Korea, July 2012.
  26. Learning Language from Ambiguous Perceptual Context
    [Details] [PDF] [Slides (PPT)]
    David L. Chen
    PhD Thesis, Department of Computer Science, University of Texas at Austin, May 2012. 196.
  27. Learning to Interpret Natural Language Navigation Instructions from Observations
    [Details] [PDF] [Slides (PPT)]
    David L. Chen and Raymond J. Mooney
    In Proceedings of the 25th AAAI Conference on Artificial Intelligence (AAAI-2011), 859-865, August 2011.
  28. Generative Alignment and Semantic Parsing for Learning from Ambiguous Supervision
    [Details] [PDF]
    Joohyun Kim and Raymond J. Mooney
    In Proceedings of the 23rd International Conference on Computational Linguistics (COLING 2010), 543--551, Beijing, China, August 2010.
  29. Learning for Semantic Parsing Using Statistical Syntactic Parsing Techniques
    [Details] [PDF] [Slides (PPT)]
    Ruifang Ge
    PhD Thesis, Department of Computer Science, University of Texas at Austin, Austin, TX, May 2010. 165 pages.
  30. Training a Multilingual Sportscaster: Using Perceptual Context to Learn Language
    [Details] [PDF]
    David L. Chen, Joohyun Kim, Raymond J. Mooney
    Journal of Artificial Intelligence Research, 37:397--435, 2010.
  31. Learning Language from Perceptual Context
    [Details] [PDF] [Slides (PPT)]
    David L. Chen
    December 2009. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  32. Learning a Compositional Semantic Parser using an Existing Syntactic Parser
    [Details] [PDF] [Slides (PPT)]
    Ruifang Ge and Raymond J. Mooney
    In Joint Conference of the 47th Annual Meeting of the Association for Computational Linguistics and the 4th International Joint Conference on Natural Language Processing of the Asian Federation of Natural Language Processing (ACL-IJCNLP 2009), 611--619, Suntec, Singapore, August 2009.
  33. A Dependency-based Word Subsequence Kernel
    [Details] [PDF]
    Rohit J. Kate
    In Proceedings of the conference on Empirical Methods in Natural Language Processing (EMNLP-2008), 400--409, Waikiki, Honolulu, Hawaii, October 2008.
  34. Transforming Meaning Representation Grammars to Improve Semantic Parsing
    [Details] [PDF]
    Rohit J. Kate
    In Proceedings of the Twelfth Conference on Computational Natural Language Learning (CoNLL-2008), 33--40, Manchester, UK, August 2008.
  35. Learning to Sportscast: A Test of Grounded Language Acquisition
    [Details] [PDF] [Slides (PPT)] [Video]
    David L. Chen and Raymond J. Mooney
    In Proceedings of the 25th International Conference on Machine Learning (ICML), Helsinki, Finland, July 2008.
  36. Learning for Semantic Parsing with Kernels under Various Forms of Supervision
    [Details] [PDF] [Slides (PPT)]
    Rohit J. Kate
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2007. 159 pages.
  37. Learning for Semantic Parsing and Natural Language Generation Using Statistical Machine Translation Techniques
    [Details] [PDF]
    Yuk Wah Wong
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, August 2007. 188 pages. Also appears as Technical Report AI07-343, Artificial Intelligence Lab, University of Texas at Austin, August 2007.
  38. Learning Language Semantics from Ambiguous Supervision
    [Details] [PDF]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the 22nd Conference on Artificial Intelligence (AAAI-07), 895-900, Vancouver, Canada, July 2007.
  39. Learning Synchronous Grammars for Semantic Parsing with Lambda Calculus
    [Details] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the 45th Annual Meeting of the Association for Computational Linguistics (ACL-2007), Prague, Czech Republic, June 2007.
  40. Semi-Supervised Learning for Semantic Parsing using Support Vector Machines
    [Details] [PDF] [Slides (PPT)]
    Rohit J. Kate and Raymond J. Mooney
    In Proceedings of the Human Language Technology Conference of the North American Chapter of the Association for Computational Linguistics, Short Papers (NAACL/HLT-2007), 81--84, Rochester, NY, April 2007.
  41. Generation by Inverting a Semantic Parser That Uses Statistical Machine Translation
    [Details] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of Human Language Technologies: The Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT-07), 172-179, Rochester, NY, 2007.
  42. Learning for Semantic Parsing
    [Details] [PDF]
    Raymond J. Mooney
    In A. Gelbukh, editors, Computational Linguistics and Intelligent Text Processing: Proceedings of the 8th International Conference (CICLing 2007), 311--324, Mexico City, Mexico, February 2007. Springer: Berlin, Germany. Invited paper.
  43. Using String-Kernels for Learning Semantic Parsers
    [Details] [PDF] [Slides (PPT)]
    Rohit J. Kate and Raymond J. Mooney
    In ACL 2006: Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the ACL, 913-920, Morristown, NJ, USA, 2006. Association for Computational Linguistics.
  44. Discriminative Reranking for Semantic Parsing
    [Details] [PDF]
    Ruifang Ge and Raymond J. Mooney
    In Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics (COLING/ACL-06), Sydney, Australia, July 2006.
  45. Learning for Semantic Parsing with Statistical Machine Translation
    [Details] [PDF]
    Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of Human Language Technology Conference / North American Chapter of the Association for Computational Linguistics Annual Meeting (HLT-NAACL-06), 439-446, New York City, NY, 2006.
  46. Learning Semantic Parsers Using Statistical Syntactic Parsing Techniques
    [Details] [PDF]
    Ruifang Ge
    2006. Doctoral Dissertation Proposal, University of Texas at Austin" , year="2006.
  47. A Kernel-based Approach to Learning Semantic Parsers
    [Details] [PDF] [Slides (PPT)]
    Rohit J. Kate
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
  48. Learning for Semantic Parsing Using Statistical Machine Translation Techniques
    [Details] [PDF]
    Yuk Wah Wong
    2005. Doctoral Dissertation Proposal, University of Texas at Austin.
  49. A Statistical Semantic Parser that Integrates Syntax and Semantics
    [Details] [PDF]
    Ruifang Ge and Raymond J. Mooney
    In Proceedings of CoNLL-2005, Ann Arbor, Michigan, June 2005.
  50. Learning to Transform Natural to Formal Languages
    [Details] [PDF] [Slides (PPT)]
    Rohit J. Kate, Yuk Wah Wong and Raymond J. Mooney
    In Proceedings of the Twentieth National Conference on Artificial Intelligence (AAAI-05), 1062-1068, Pittsburgh, PA, July 2005.
  51. Learning Transformation Rules for Semantic Parsing
    [Details] [PDF]
    Rohit J. Kate, Yuk Wah Wong, Ruifang Ge, and Raymond J. Mooney
    April 2004. Unpublished Technical Report.
  52. Learning Semantic Parsers: An Important But Under-Studied Problem
    [Details] [PDF]
    Raymond J. Mooney
    In Papers from the AAAI 2004 Spring Symposium on Language Learning: An Interdisciplinary Perspective, 39--44, Stanford, CA, March 2004.
  53. Integrating Top-down and Bottom-up Approaches in Inductive Logic Programming: Applications in Natural Language Processing and Relational Data Mining
    [Details] [PDF]
    Lappoon R. Tang
    PhD Thesis, Department of Computer Sciences, University of Texas, Austin, TX, August 2003.
  54. Acquiring Word-Meaning Mappings for Natural Language Interfaces
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Journal of Artificial Intelligence Research, 18:1-44, 2003.
  55. Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
    [Details] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    In Proceedings of the 12th European Conference on Machine Learning, 466-477, Freiburg, Germany, 2001.
  56. Automated Construction of Database Interfaces: Integrating Statistical and Relational Learning for Semantic Parsing
    [Details] [PDF]
    Lappoon R. Tang and Raymond J. Mooney
    In Proceedings of the Joint SIGDAT Conference on Empirical Methods in Natural Language Processing and Very Large Corpora(EMNLP/VLC-2000), 133-141, Hong Kong, October 2000.
  57. Integrating Statistical and Relational Learning for Semantic Parsing: Applications to Learning Natural Language Interfaces for Databases
    [Details] [PDF]
    Lappoon R. Tang
    May 2000. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.
  58. Learning for Semantic Interpretation: Scaling Up Without Dumbing Down
    [Details] [PDF]
    Raymond J. Mooney
    In Workshop Notes for the Workshop on Learning Language in Logic, 7-15, Bled, Slovenia, 2000.
  59. Automatic Construction of Semantic Lexicons for Learning Natural Language Interfaces
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    In Proceedings of the Sixteenth National Conference on Artificial Intelligence (AAAI-99), 487-493, Orlando, FL, July 1999.
  60. Active Learning for Natural Language Parsing and Information Extraction
    [Details] [PDF]
    Cynthia A. Thompson, Mary Elaine Califf and Raymond J. Mooney
    In Proceedings of the Sixteenth International Conference on Machine Learning (ICML-99), 406-414, Bled, Slovenia, June 1999.
  61. Semantic Lexicon Acquisition for Learning Natural Language Interfaces
    [Details] [PDF]
    Cynthia Ann Thompson
    PhD Thesis, Department of Computer Sciences, University of Texas at Austin, Austin, TX, December 1998. 101 pages. Also appears as Technical Report AI 99-278, Artificial Intelligence Lab, University of Texas at Austin.
  62. Semantic Lexicon Acquisition for Learning Natural Language Interfaces
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    In Proceedings of the Sixth Workshop on Very Large Corpora, Montreal, Quebec, Canada, August 1998. Also available as TR AI 98-273, Artificial Intelligence Lab, University of Texas at Austin, May 1998.
  63. Learning to Parse Natural Language Database Queries into Logical Form
    [Details] [PDF]
    Cynthia A. Thompson, Raymond J. Mooney, and Lappoon R. Tang
    In Proceedings of the ML-97 Workshop on Automata Induction, Grammatical Inference, and Language Acquisition, Nashville, TN, July 1997.
  64. An Inductive Logic Programming Method for Corpus-based Parser Construction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    January 1997. Unpublished Technical Note.
  65. Semantic Lexicon Acquisition for Learning Parsers
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    1997. Submitted for review.
  66. Inductive Logic Programming for Natural Language Processing
    [Details] [PDF]
    Raymond J. Mooney
    In Stephen Muggleton, editors, Inductive Logic Programming: Selected papers from the 6th International Workshop, 3-22, Berlin, 1996. Springer Verlag.
  67. Learning to Parse Database Queries using Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In AAAI/IAAI, 1050-1055, Portland, OR, August 1996. AAAI Press/MIT Press.
  68. Corpus-Based Lexical Acquisition For Semantic Parsing
    [Details] [PDF]
    Cynthia Thompson
    February 1996. Ph.D. proposal.
  69. Lexical Acquisition: A Novel Machine Learning Problem
    [Details] [PDF]
    Cynthia A. Thompson and Raymond J. Mooney
    Technical Report, Artificial Intelligence Lab, University of Texas at Austin, January 1996.
  70. Using Inductive Logic Programming to Automate the Construction of Natural Language Parsers
    [Details] [PDF]
    John M. Zelle
    PhD Thesis, Department of Computer Sciences, The University of Texas at Austin, Austin, TX, 1995.
  71. A Comparison of Two Methods Employing Inductive Logic Programming for Corpus-based Parser Constuction
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Working Notes of the IJCAI-95 Workshop on New Approaches to Learning for Natural Language Processing, 79--86, Montreal, Quebec, Canada, August 1995.
  72. Acquisition of a Lexicon from Semantic Representations of Sentences
    [Details] [PDF]
    Cynthia A. Thompson
    In Proceedings of the 33rd Annual Meeting of the Association for Computational Linguistics (ACL-95), 335-337, Cambridge, MA, 1995.
  73. Learning Semantic Grammars With Constructive Inductive Logic Programming
    [Details] [PDF]
    John M. Zelle and Raymond J. Mooney
    In Proceedings of the 11th National Conference on Artificial Intelligence, 817-822, 1993. Menlo Park, CA: AAAI Press.
  74. Learning Search-Control Heuristics for Logic Programs: Applications to Speedup Learning and Language Acquisition
    [Details] [PDF]
    John M. Zelle
    March 1993. Ph.D. proposal, Department of Computer Sciences, University of Texas at Austin.