Instance-based natural language generation

Instance-Based Natural Language Generation

We investigate the use of instance-based ranking methods for surface realization in natural language generation. Our approach to instance-based natural language generation (IBNLG) employs two components: a rule system that ‘overgenerates’ a number of realization candidates from a meaning representation and an instance-based ranker that scores the candidates according to their similarity to exam...

Fluency and Completeness in Instance-based Natural Language Generation

Instance-based Sentence Boundary Determination by Optimization for Natural Language Generation

This paper describes a novel instancebased sentence boundary determination method for natural language generation that optimizes a set of criteria based on examples in a corpus. Compared to existing sentence boundary determination approaches, our work offers three significant contributions. First, our approach provides a general domain independent framework that effectively addresses sentence b...

Designing a Speech Corpus for Instance-based Spoken Language Generation

In spoken language applications such as conversation systems where not only the speech waveforms but also the content of the speech (the text) need to be generated automatically, a Concept-to-Speech (CTS) system is needed. In this paper, we address several issues on designing a speech corpus to facilitate an instance-based integrated CTS platform. Both the instance-based CTS generation approach...

Model-Based Generation of Natural Language Specifications

Application of formal models provides many benefits for the software and system development, however, the learning curve of formal languages could be a critical factor for an industrial project. Thus, a natural language specification that reflects all the aspects of the formal model might help to understand the model and be especially useful for the stakeholders who do not know the correspondin...