Type-logical semantics
The syntactic process
Conditional Random Fields: Probabilistic Models for Segmenting and Labeling Sequence Data
ICML '01 Proceedings of the Eighteenth International Conference on Machine Learning
A fully statistical approach to natural language interfaces
ACL '96 Proceedings of the 34th annual meeting on Association for Computational Linguistics
Estimators for stochastic "Unification-Based" grammars
ACL '99 Proceedings of the 37th annual meeting of the Association for Computational Linguistics on Computational Linguistics
DATE: a dialogue act tagging scheme for evaluation of spoken dialogue systems
HLT '01 Proceedings of the first international conference on Human language technology research
EMNLP '00 Proceedings of the 2000 Joint SIGDAT conference on Empirical methods in natural language processing and very large corpora: held in conjunction with the 38th Annual Meeting of the Association for Computational Linguistics - Volume 13
New developments in parsing technology
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
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
Semantic parsing with structured SVM ensemble classification models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Automatic annotation of context and speech acts for dialogue corpora
Natural Language Engineering
Deep linguistic processing for spoken dialogue systems
DeepLP '07 Proceedings of the Workshop on Deep Linguistic Processing
A generative model for parsing natural language to meaning representations
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
The Hidden Information State model: A practical framework for POMDP-based spoken dialogue management
Computer Speech and Language
Optimizing dialogue management with reinforcement learning: experiments with the NJFun system
Journal of Artificial Intelligence Research
Acquiring word-meaning mappings for natural language interfaces
Journal of Artificial Intelligence Research
Using Natural Language Processing to Analyze Tutorial Dialogue Corpora Across Domains Modalities
Proceedings of the 2009 conference on Artificial Intelligence in Education: Building Learning Systems that Care: From Knowledge Representation to Affective Modelling
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
Learning context-dependent mappings from sentences to logical form
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 2 - Volume 2
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Following directions using statistical machine translation
Proceedings of the 5th ACM/IEEE international conference on Human-robot interaction
Unsupervised ontology induction from text
ACL '10 Proceedings of the 48th Annual Meeting of the Association for Computational Linguistics
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
Training a multilingual sportscaster: using perceptual context to learn language
Journal of Artificial Intelligence Research
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 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
A semi supervised learning model for mapping sentences to logical form with ambiguous supervision
NLDB'12 Proceedings of the 17th international conference on Applications of Natural Language Processing and Information Systems
Fast online lexicon learning for grounded language acquisition
ACL '12 Proceedings of the 50th Annual Meeting of the Association for Computational Linguistics: Long Papers - Volume 1
Learning to interpret natural language instructions
SIAC '12 Proceedings of the Second Workshop on Semantic Interpretation in an Actionable Context
Learning dependency-based compositional semantics
Computational Linguistics
Introduction to the special issue on learning semantics
Machine Learning
Learning from natural instructions
Machine Learning
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Conversations provide rich opportunities for interactive, continuous learning. When something goes wrong, a system can ask for clarification, rewording, or otherwise redirect the interaction to achieve its goals. In this paper, we present an approach for using conversational interactions of this type to induce semantic parsers. We demonstrate learning without any explicit annotation of the meanings of user utterances. Instead, we model meaning with latent variables, and introduce a loss function to measure how well potential meanings match the conversation. This loss drives the overall learning approach, which induces a weighted CCG grammar that could be used to automatically bootstrap the semantic analysis component in a complete dialog system. Experiments on DARPA Communicator conversational logs demonstrate effective learning, despite requiring no explicit meaning annotations.