Syntax-based grammaticality improvement using CCG and guided search

Authors:
Yue Zhang;Stephen Clark
Affiliations:
University of Cambridge;University of Cambridge
Venue:
EMNLP '11 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Year:
2011

Citing 17
Cited 2

The syntactic process

The syntactic process
New figures of merit for best-first probabilistic chart parsing

Computational Linguistics
Parsing the wall street journal using a Lexical-Functional Grammar and discriminative estimation techniques

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
BLEU: a method for automatic evaluation of machine translation

ACL '02 Proceedings of the 40th Annual Meeting on Association for Computational Linguistics
Parsing with generative models of predicate-argument structure

ACL '03 Proceedings of the 41st Annual Meeting on Association for Computational Linguistics - Volume 1
Learning as search optimization: approximate large margin methods for structured prediction

ICML '05 Proceedings of the 22nd international conference on Machine learning
Evaluation metrics for generation

INLG '00 Proceedings of the first international conference on Natural language generation - Volume 14
Discriminative training methods for hidden Markov models: theory and experiments with perceptron algorithms

EMNLP '02 Proceedings of the ACL-02 conference on Empirical methods in natural language processing - Volume 10
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
Improving grammaticality in statistical sentence generation: introducing a dependency spanning tree algorithm with an argument satisfaction model

EACL '09 Proceedings of the 12th Conference of the European Chapter of the Association for Computational Linguistics
LTAG dependency parsing with bidirectional incremental construction

EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
Perceptron reranking for CCG realization

EMNLP '09 Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing: Volume 1 - Volume 1
Statistical Machine Translation

Statistical Machine Translation
Instance-based natural language generation

Natural Language Engineering
An efficient algorithm for easy-first non-directional dependency parsing

HLT '10 Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics
Fluency constraints for minimum Bayes-risk decoding of statistical machine translation lattices

COLING '10 Proceedings of the 23rd International Conference on Computational Linguistics

Syntax-based word ordering incorporating a large-scale language model

EACL '12 Proceedings of the 13th Conference of the European Chapter of the Association for Computational Linguistics
Partial-tree linearization: generalized word ordering for text synthesis

IJCAI'13 Proceedings of the Twenty-Third international joint conference on Artificial Intelligence

Quantified Score

Hi-index	0.01

Visualization

Abstract

Machine-produced text often lacks grammaticality and fluency. This paper studies grammaticality improvement using a syntax-based algorithm based on ccg. The goal of the search problem is to find an optimal parse tree among all that can be constructed through selection and ordering of the input words. The search problem, which is significantly harder than parsing, is solved by guided learning for best-first search. In a standard word ordering task, our system gives a BLEU score of 40.1, higher than the previous result of 33.7 achieved by a dependency-based system.