A Statistical, Grammar-Based Approach to Microplanning
Although 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 microplanning between aggregation, surface realization, and sentence segmentation. In this article, we propose a hybrid symbolic/statistical approach to jointly model the constraints regulating 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-based 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.
Code (0)
등록된 구현이 없습니다.
Tasks
SentenceSentence segmentationText GenerationMethods 이 논문이 사용한 방법론
Similar Papers 제목 키워드 기반
Statistical Parsing of Tree Wrapping Grammars
We describe an approach to statistical parsing with Tree-Wrapping Grammars (TWG). TWG is a tree-rewriting formalism which includes the tree-combination operations of substitution, sister-adjunction and tree-wrapping subs…
The WebNLG Challenge: Generating Text from RDF Data
The WebNLG challenge consists in mapping sets of RDF triples to text. It provides a common benchmark on which to train, evaluate and compare {``}microplanners{''}, i.e. generation systems that verbalise a given content b…
Referring ExpressionReferring expression generationSentenceSentence segmentation+1ML-Optimization of Ported Constraint Grammars
In this paper, we describe how a Constraint Grammar with linguist-written rules can be optimized and ported to another language using a Machine Learning technique. The effects of rule movements, sorting, grammar-sectioni…
Domain AdaptationMorphological AnalysisSparse Logistic Regression with High-order Features for Automatic Grammar Rule Extraction from Treebanks
Descriptive grammars are highly valuable, but writing them is time-consuming and difficult. Furthermore, while linguists typically use corpora to create them, grammar descriptions often lack quantitative data. As for for…
DescriptiveOn Unsupervised Training of Link Grammar Based Language Models
In this short note we explore what is needed for the unsupervised training of graph language models based on link grammars. First, we introduce the ter-mination tags formalism required to build a language model based on …
Language ModelingLanguage ModellingText Generation