Flexible Natural Language Generation in Multiple Contexts

We present a practical approach to Natural Language Generation (NLG) for spoken dialogue systems. The approach is based on small template fragments (mini-templates). The system’s object architecture facilitates generation of phrases across pre-defined business domains and registers, as well as into different languages. The architecture simplifies NLG in well-understood application contexts, while providing the flexibility for a developer and for the system, to vary linguistic output according to dialogue context, including any intended affective impact. Mini-templates are used with a suite of domain term objects, resulting in an NLG system (MINTGEN – MINi-Template GENerator) whose extensibility and ease of maintenance is enhanced by the sparsity of information devoted to individual domains. The system also avoids the need for specialist linguistic competence on the part of the system maintainer.

Development of a Java-based Unified and Flexible Natural Language Discourse System

This paper outlines the design and development of a Java-based, unified and flexible natural language dialogue system that enables users to interact using natural language, e.g. speech. A number of software development issues are considered with the aim of designing an architecture that enables different discourse components to be readily and flexibly combined in a manner that permits information to be easily shared. Use of XML schemas assists this component interaction. The paper describes how a range of Java language features were employed to support the development of the architecture, providing an illustration of how a modern programming language makes tractable the development of a complex dialogue system.

Extending Zipf’s law to n-grams for large corpora

Experiments show that for a large corpus, Zipf’s law does not hold for all rank of words: the frequencies fall below those predicted by Zipf’s law for ranks greater than about 5,000 word types in the English language and about 30,000 word types in the inflected languages Irish and Latin. It also does not hold for syllables or words in the syllable-based languages, Chinese or Vietnamese. However, when single words are combined together with word n-grams in one list and put in rank order, the frequency of tokens in the combined list extends Zipf’s law with a slope close to -1 on a log-log plot in all five languages. Further experiments have demonstrated the validity of this extension of Zipf’s law to n-grams of letters, phonemes or binary bits in English. It is shown theoretically that probability theory
alone can predict this behavior in randomly created n-grams of binary bits.