The syntactic process
Exploiting a probabilistic hierarchical model for generation
COLING '00 Proceedings of the 18th conference on Computational linguistics - Volume 1
Coupling CCG and hybrid logic dependency semantics
ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
The Proposition Bank: An Annotated Corpus of Semantic Roles
Computational Linguistics
Discriminative language modeling with conditional random fields and the perceptron algorithm
ACL '04 Proceedings of the 42nd Annual Meeting on Association for Computational Linguistics
Robust PCFG-based generation using automatically acquired LFG approximations
ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
CCGbank: A Corpus of CCG Derivations and Dependency Structures Extracted from the Penn Treebank
Computational Linguistics
Wide-coverage efficient statistical parsing with ccg and log-linear models
Computational Linguistics
Dependency-based n-gram models for general purpose sentence realisation
COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
A more precise analysis of punctuation for broad-coverage surface realization with CCG
GEAF '08 Proceedings of the Workshop on Grammar Engineering Across Frameworks
Probabilistic models for disambiguation of an HPSG-based chart generator
Parsing '05 Proceedings of the Ninth International Workshop on Parsing Technology
Perceptron reranking for CCG realization
EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Glue rules for robust chart realization
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
The OSU system for surface realization at Generation Challenges 2011
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Minimal dependency length in realization ranking
EMNLP-CoNLL '12 Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning
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This paper shows that incorporating linguistically motivated features to ensure correct animacy and number agreement in an averaged perceptron ranking model for CCG realization helps improve a state-of-the-art baseline even further. Traditionally, these features have been modelled using hard constraints in the grammar. However, given the graded nature of grammaticality judgements in the case of animacy we argue a case for the use of a statistical model to rank competing preferences. Though subject-verb agreement is generally viewed to be syntactic in nature, a perusal of relevant examples discussed in the theoretical linguistics literature (Kathol, 1999; Pollard and Sag, 1994) points toward the heterogeneous nature of English agreement. Compared to writing grammar rules, our method is more robust and allows incorporating information from diverse sources in realization. We also show that the perceptron model can reduce balanced punctuation errors that would otherwise require a post-filter. The full model yields significant improvements in BLEU scores on Section 23 of the CCGbank and makes many fewer agreement errors.