A simple domain-independent probabilistic approach to generation

Authors:
Gabor Angeli;Percy Liang;Dan Klein
Affiliations:
UC Berkeley, Berkeley, CA;UC Berkeley, Berkeley, CA;UC Berkeley, Berkeley, CA
Venue:
EMNLP '10 Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing
Year:
2010

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Abstract

We present a simple, robust generation system which performs content selection and surface realization in a unified, domain-independent framework. In our approach, we break up the end-to-end generation process into a sequence of local decisions, arranged hierarchically and each trained discriminatively. We deployed our system in three different domains---Robocup sportscasting, technical weather forecasts, and common weather forecasts, obtaining results comparable to state-of-the-art domain-specific systems both in terms of BLEU scores and human evaluation.