Centering: a framework for modeling the local coherence of discourse
Computational Linguistics
Trainable methods for surface natural language generation
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Human evaluation of a German surface realisation ranker
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Incorporating information status into generation ranking
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Developing German semantics on the basis of parallel LFG grammars
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Beyond NomBank: a study of implicit arguments for nominal predicates
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Comparing rating scales and preference judgements in language evaluation
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Finding common ground: towards a surface realisation shared task
INLG '10 Proceedings of the 6th International Natural Language Generation Conference
Broad coverage multilingual deep sentence generation with a stochastic multi-level realizer
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Evaluating salience metrics for the context-adequate realization of discourse referents
ENLG '11 Proceedings of the 13th European Workshop on Natural Language Generation
Lfg generation by grammar specialization
Computational Linguistics
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This paper addresses a data-driven surface realisation model based on a large-scale reversible grammar of German. We investigate the relationship between the surface realisation performance and the character of the input to generation, i.e. its degree of underspecification. We extend a syntactic surface realisation system, which can be trained to choose among word order variants, such that the candidate set includes active and passive variants. This allows us to study the interaction of voice and word order alternations in realistic German corpus data. We show that with an appropriately underspecified input, a linguistically informed realisation model trained to regenerate strings from the underlying semantic representation achieves 91.5% accuracy (over a baseline of 82.5%) in the prediction of the original voice.