A Statistical, Grammar-Based Approach to Micro-Planning
While there has been much work in recent years on data-driven natural language generation, little attention has been paid to the fine grained interactions that arise during micro-planning between aggregation, surface realization and sentence segmentation. In this talk, I will argue for a hybrid symbolic/statistical approach to jointly model the interactions arising in Natural Language Generation between syntactic, aggregation and sentence segmentation choices. The approach integrates a small hand-written grammar, a statistical hypertagger and a surface realization algorithm. It is applied to the verbalization of knowledge base queries and tested on 13 knowledge bases to demonstrate domain independence. We evaluate our approach in several ways. A quantitative analysis shows that the hybrid approach outperforms a purely symbolic approach in terms of both speed and coverage. Results from a human study indicate that users find the output of this hybrid
statistic/symbolic system more fluent than both a template- and a purely symbolic grammar-based approach. Finally, we illustrate by means of examples that our approach can account for various factors impacting aggregation, sentence segmentation and surface realization.
(Joint work with Laura Perez-Beltrachini)
Thursday, 11:30h, September 17th 2015