Type-logical semantics
The syntactic process
POPL '77 Proceedings of the 4th ACM SIGACT-SIGPLAN symposium on Principles of programming languages
Towards a theory of natural language interfaces to databases
Proceedings of the 8th international conference on Intelligent user interfaces
Using Multiple Clause Constructors in Inductive Logic Programming for Semantic Parsing
EMCL '01 Proceedings of the 12th European Conference on Machine Learning
Constraint Processing
Head-driven statistical models for natural language parsing
Head-driven statistical models for natural language parsing
Building a large annotated corpus of English: the penn treebank
Computational Linguistics - Special issue on using large corpora: II
An efficient easily adaptable system for interpreting natural language queries
Computational Linguistics
A fully statistical approach to natural language interfaces
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Coupling CCG and hybrid logic dependency semantics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning accurate, compact, and interpretable tree annotation
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
QuestionBank: creating a corpus of parse-annotated questions
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Using string-kernels for learning semantic parsers
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Wide-coverage semantic representations from a CCG parser
COLING '04 Proceedings of the 20th international conference on Computational Linguistics
Learning for semantic parsing with statistical machine translation
HLT-NAACL '06 Proceedings of the main conference on Human Language Technology Conference of the North American Chapter of the Association of Computational Linguistics
Learning to sportscast: a test of grounded language acquisition
Proceedings of the 25th international conference on Machine learning
Graphical Models, Exponential Families, and Variational Inference
Foundations and Trends® in Machine Learning
Search-based structured prediction
Machine Learning
A generative model for parsing natural language to meaning representations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Learning to transform natural to formal languages
AAAI'05 Proceedings of the 20th national conference on Artificial intelligence - Volume 3
Learning language semantics from ambiguous supervision
AAAI'07 Proceedings of the 22nd national conference on Artificial intelligence - Volume 1
Online EM for unsupervised models
NAACL '09 Proceedings of Human Language Technologies: The 2009 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Reinforcement learning for mapping instructions to actions
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Learning semantic correspondences with less supervision
ACL '09 Proceedings of the Joint Conference of the 47th Annual Meeting of the ACL and the 4th International Joint Conference on Natural Language Processing of the AFNLP: Volume 1 - Volume 1
Reading to learn: constructing features from semantic abstracts
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 2 - Volume 2
A statistical semantic parser that integrates syntax and semantics
CONLL '05 Proceedings of the Ninth Conference on Computational Natural Language Learning
Learning to follow navigational directions
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Reading between the lines: learning to map high-level instructions to commands
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
Learning to parse database queries using inductive logic programming
AAAI'96 Proceedings of the thirteenth national conference on Artificial intelligence - Volume 2
Driving semantic parsing from the world's response
CoNLL '10 Proceedings of the Fourteenth Conference on Computational Natural Language Learning
Inducing probabilistic CCG grammars from logical form with higher-order unification
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Learning to win by reading manuals in a Monte-Carlo framework
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Learning dependency-based compositional semantics
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Confidence driven unsupervised semantic parsing
HLT '11 Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies - Volume 1
Deterministic statistical mapping of sentences to underspecified semantics
IWCS '11 Proceedings of the Ninth International Conference on Computational Semantics
Bootstrapping semantic parsers from conversations
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Lexical generalization in CCG grammar induction for semantic parsing
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Semantic mapping between natural language questions and SQL queries via syntactic pairing
NLDB'09 Proceedings of the 14th international conference on Applications of Natural Language to Information Systems
Learning from natural instructions
IJCAI'11 Proceedings of the Twenty-Second international joint conference on Artificial Intelligence - Volume Volume Three
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Suppose we want to build a system that answers a natural language question by representing its semantics as a logical forxm and computing the answer given a structured database of facts. The core part of such a system is the semantic parser that maps questions to logical forms. Semantic parsers are typically trained from examples of questions annotated with their target logical forms, but this type of annotation is expensive. Our goal is to instead learn a semantic parser from question-answer pairs, where the logical form is modeled as a latent variable. We develop a new semantic formalism, dependency-based compositional semantics DCS and define a log-linear distribution over DCS logical forms. The model parameters are estimated using a simple procedure that alternates between beam search and numerical optimization. On two standard semantic parsing benchmarks, we show that our system obtains comparable accuracies to even state-of-the-art systems that do require annotated logical forms.