ACL-44 Proceedings of the 21st International Conference on Computational Linguistics and the 44th annual meeting of the Association for Computational Linguistics
Discourse generation using utility-trained coherence models
COLING-ACL '06 Proceedings of the COLING/ACL on Main conference poster sessions
Abstractive headline generation using WIDL-expressions
Information Processing and Management: an International Journal
Automatic prediction of parser accuracy
EMNLP '08 Proceedings of the Conference on Empirical Methods in Natural Language Processing
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In this dissertation, I propose a new natural language generation paradigm, based on direct transformation of textual information into well-formed textual output. I support this language generation paradigm with theoretical contributions in the field of formal languages, new algorithms, empirical results, and software implementations. At the core of this work is a novel representation formalism for probability distributions over finite languages. Due to its convenient representation and computational properties, this formalism supports a wide range of language generation needs, from sentence realization to text planning. Based on this formalism, I describe, implement, and analyze theoretically a family of algorithms that perform language generation using direct transformations of text. These algorithms use stochastic models of language to drive the generation process. I perform extensive empirical evaluations using my implementation of these algorithms. These evaluations show state-of-the-art performance in automatic translation, and significant improvements in state-of-the-art performance in abstractive headline generation and coherent discourse generation.