On Combining Language Models to Improve a Text-Based Human-Machine Interface

This paper concentrates on improving a text-based human-machine interface integrated into a robotic wheelchair. Since word prediction is one of the most common methods used in such systems, the goal of this work is to improve the results using this specific module. For this, an exponential interpola...

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Bibliographic Details
Published inInternational journal of advanced robotic systems Vol. 12; no. 12
Main Authors Cavalieri, Daniel Cruz, Bastos-Filho, Teodiano, Palazuelos-Cagigas, Sira Elena, Sarcinelli-Filho, Mario
Format Journal Article
LanguageEnglish
Published London, England SAGE Publications 02.12.2015
Sage Publications Ltd
SAGE Publishing
Subjects
Communication Aid
Human-machine Interfaces
Word Prediction Systems
Language Modelling
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Summary:This paper concentrates on improving a text-based human-machine interface integrated into a robotic wheelchair. Since word prediction is one of the most common methods used in such systems, the goal of this work is to improve the results using this specific module. For this, an exponential interpolation language model (LM) is considered. First, a model based on partial differential equations is proposed; with the appropriate initial conditions, we are able to design a interpolation language model that merges a word-based n-gram language model and a part-of-speech-based language model. Improvements in keystroke saving (KSS) and perplexity (PP) over the word-based n-gram language model and two other traditional interpolation models are obtained, considering two different task domains and three different languages. The proposed interpolation model also provides additional improvements over the hit rate (HR) parameter.
ISSN:1729-8806
1729-8814
DOI:10.5772/61753