Learning distributions over logical forms for referring expression generation

EMNLP 2013 - 2013 Conference on Empirical Methods in Natural Language Processing, Proceedings of the Conference

Volumn , Issue , 2013, Pages 1914-1925

  FitzGerald, Nicholas ^a   Artzi, Yoav ^a   Zettlemoyer, Luke ^a  

^a University of Washington   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

APPROXIMATE INFERENCE; DENSITY ESTIMATION; EFFECTIVE LEARNING; LINEAR DISTRIBUTION; LOGICAL EXPRESSIONS; REFERRING EXPRESSIONS; RELATIVE ERRORS; STATE OF THE ART;

NATURAL LANGUAGE PROCESSING SYSTEMS;

EID: 84908171707     PISSN: None     EISSN: None     Source Type: Conference Proceeding    
DOI: None     Document Type: Conference Paper

References (43)

1. Areces, C., Koller, A., and Striegnitz, K. (2008). Referring expressions as formulas of description logic. In Proceedings of the International Natural Language Generation Conference.
2. Artzi, Y. and Zettlemoyer, L. (2011). Bootstrapping semantic parsers from conversations. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
3. Artzi, Y. and Zettlemoyer, L. (2013a). UW SPF: The University of Washington Semantic Parsing Framework.
4. Artzi, Y. and Zettlemoyer, L. (2013b). Weakly supervised learning of semantic parsers for mapping instructions to actions. Transactions of the Association for Computational Linguistics, 1(1):49-62.
5. Barzilay, R. and Lapata, M. (2005). Collective content selection for concept-to-text generation. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
6. Berg, A. C., and Berg, T. L., Daume, H., Dodge, J., Goyal, A., Han, X., Mensch, A., Mitchell, M., Sood, A., Stratos, K., et al. (2012). Understanding and predicting importance in images. In IEEE Conference on Computer Vision and Pattern Recognition.
7. Carenini, G., and Ng, R. T., and Pauls, A. (2006). Multi-document summarization of evaluative text. In Proceedings of the Conference of the European Chapter of the Association for Computational Linguistics.
8. Carpenter, B. (1997). Type-Logical Semantics. The MIT Press.
9. Chen, D., Kim, J., and Mooney, R. (2010). Training a multilingual sportscaster: using perceptual context to learn language. Journal of Artificial Intelligence Research, 37(1):397-436.
10. Chen, D. and Mooney, R. (2011). Learning to interpret natural language navigation instructions from observations. In Proceedings of the National Conference on Artificial Intelligence.
11. Dale, R. and Reiter, E. (1995). Computational interpretations of the gricean maxims in the generation of referring expressions. Cognitive Science, 19:233-264.
12. Dale, R. and Reiter, E. (2000). Building natural language generation systems. Cambridge University Press.
13. Gardent, C. (2002). Generating minimal definite descriptions. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
14. Gatt, A. and van Deemter, K. (2007). Incremental generation of plural descriptions: Similarity and partitioning. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
15. Gatt, A., Van Der Sluis, I., and Van Deemter, K. (2007). Evaluating algorithms for the generation of referring expressions using a balanced corpus. In Proceedings of the European Workshop on Natural Language Generation.
16. Grice, H. P. (1975). Logic and conversation. 1975, pages 41-58.
17. Horacek, H. (2004). On referring to sets of objects naturally. In Natural Language Generation, pages 70-79. Springer.
18. Kim, J. and Mooney, R. J. (2012). Unsupervised PCFG induction for grounded language learning with highly ambiguous supervision. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
19. Konstas, I. and Lapata, M. (2012). Unsupervised concept-to-text generation with hypergraphs. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics.
20. Krahmer, E. and van Deemter, K. (2012). Computational generation of referring expressions: A survey. Computational Linguistics, 38(1):173-218.
21. Krishnamurthy, J. and Kollar, T. (2013). Jointly learning to parse and perceive: Connecting natural language to the physical world. Transactions of the Association for Computational Linguistics, 1(2):193-206.
22. Liang, P., Jordan, M., and Klein, D. (2009). Learning semantic correspondences with less supervision. In Proceedings of the Annual Meeting of the Association for Computational Linguistics.
23. Lu, W. and Ng, H. T. (2011). A probabilistic forest-to-string model for language generation from typed lambda calculus expressions. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
24. Matuszek, C., FitzGerald, N., Zettlemoyer, L., Bo, L., and Fox, D. (2012a). A joint model of language and perception for grounded attribute learning. Proceedings of the International Conference on Machine Learning.
25. Matuszek, C., Herbst, E., Zettlemoyer, L. S., and Fox, D. (2012b). Learning to parse natural language commands to a robot control system. In Proceedings of the International Symposium on Experimental Robotics.
26. Mitchell, M., van Deemter, K., and Reiter, E. (2011a). Applying machine learning to the choice of size modifiers. In Proceedings of the PRE-CogSci Workshop.
27. Mitchell, M., Van Deemter, K., and Reiter, E. (2011b). Two approaches for generating size modifiers. In Proceedings of the European Workshop on Natural Language Generation.
28. Mitchell, M., van Deemter, K., and Reiter, E. (2013). Generating expressions that refer to visible objects. In Proceedings of Conference of the North American Chapter of the Association for Computational Linguistics.
29. Ren, Y., Van Deemter, K., and Pan, J. Z. (2010). Charting the potential of description logic for the generation of referring expressions. In Proceedings of the International Natural Language Generation Conference.
30. Scontras, G., Graff, P., and Goodman, N. D. (2012). Comparing pluralities. Cognition, 123(1):190-197.
31. Steedman, M. (2011). Taking Scope. The MIT Press.
32. Stone, M. (2000). On identifying sets. In Proceedings of the International Conference on Natural Language Generation.
33. van Deemter, K. (2002). Generating referring expressions: Boolean extensions of the incremental algorithm. Computational Linguistics, 28:37-52.
34. van Deemter, K., Gatt, A., Sluis, I. v. d., and Power, R. (2012a). Generation of referring expressions: Assessing the incremental algorithm. Cognitive Science, 36(5):799-836.
35. van Deemter, K., Gatt, A., van Gompel, R. P., and Krahmer, E. (2012b). Toward a computational psycholinguistics of reference production. Topics in Cognitive Science, 4(2):166-183.
36. Viethen, J. and Dale, R. (2010). Speaker-dependent variation in content selection for referring expression generation. In Proceedings of the Australasian Language Technology Workshop.
37. Viethen, J., Mitchell, M., and Krahmer, E. (2013). Graphs and spatial relations in the generation of referring expressions. In Proceedings of the European Workshop on Natural Language Generation.
38. White, M. and Rajkumar, R. (2009). Perceptron reranking for ccg realization. In Proceedings of the Conference on Empirical Methods in Natural Language Processing.
39. Winograd, T. (1972). Understanding natural language. Cognitive Psychology, 3(1):1-191.
40. Zelle, J. and Mooney, R. (1996). Learning to parse database queries using inductive logic programming. In Proceedings of the National Conference on Artificial Intelligence.
41. Zettlemoyer, L. and Collins, M. (2005). Learning to map sentences to logical form: Structured classification with probabilistic categorial grammars. In Proceedings of the Conference on Uncertainty in Artificial Intelligence.
42. Zettlemoyer, L. and Collins, M. (2007). Online learning of relaxed CCG grammars for parsing to logical form. In Proceedings of the Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning.
43. Zitnick, C. L. and Parikh, D. (2013). Bringing semantics into focus using visual abstraction. In IEEE Conference on Computer Vision and Pattern Recognition.