A preprocessor for an English-to-Sign Language Machine Translation system
Thesis (MSc (Computer Science))--University of Stellenbosch, 2005.
Sign Languages such as South African Sign Language, are proper natural languages; they have their own vocabularies, and they make use of their own grammar rules. However, machine translation from a spoken to a signed language creates interesting challenges. These problems are caused as a result of the differences in character between spoken and signed languages. Sign Languages are classified as visual-spatial languages: a signer makes use of the space around him, and gives visual clues from body language, facial expressions and sign movements to help him communicate. It is the absence of these elements in the written form of a spoken language that causes the contextual ambiguities during machine translation. The work described in this thesis is aimed at resolving the ambiguities caused by a translation from written English to South African Sign Language. We designed and implemented a preprocessor that uses areas of linguistics such as anaphora resolution and a data structure called a scene graph to help with the spatial aspect of the translation. The preprocessor also makes use of semantic and syntactic analysis, together with the help of a semantic relational database, to find emotional context from text. This analysis is then used to suggest body language, facial expressions and sign movement attributes, helping us to address the visual aspect of the translation. The results show that the system is flexible enough to be used with different types of text, and will overall improve the quality of a machine translation from English into a Sign Language.