A Statistical, Grammar-Based Approach to Micro-Planning

Claire Gardent (1) , Laura Perez-Beltrachini (1)

1 SYNALP - Natural Language Processing : representations, inference and semantics

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 these interactions. Our approach integrates a small handwritten 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.

Domaines: Informatique [cs]

Claire Gardent, Laura Perez-Beltrachini. A Statistical, Grammar-Based Approach to Micro-Planning. Computational Linguistics, 2017, ⟨10.1162/COLI_a_00273⟩. ⟨hal-01623758⟩