Date : 15 December 2015
Location: Brown Bag Seminar Series at the U.S. National Library of Medicine, Bethesda, Maryland (U.S.A)
Speaker: Claire Gardent
Title: Making Choices: A Grammar-Based Statistical Approach to Microplanning
Abstract: 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 paper, we propose a hybrid symbolic/statistical approach to jointly model the interactions arising in Natural Language Generation between syntactic, aggregation and sentence segmentation choices. Our 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.