Representation of american sign language for machine translation

This dissertation describes an approach to designing a machine translation system that generates a representation of American Sign Language (ASL) from English. ASL uses space and non-manual signals (NMSs) to encode grammatical features such as agreement, negation, wh-questions, etc. Previous computational systems for ASL are typically hindered by static representations of ASL signs, which makes it computationally impractical to represent the large number of possible surface forms for each sign, and by the use of notation systems that cannot represent such variation. The approach developed here addresses these limitations. The representation of ASL is based on the Move-Hold (MH) model (Liddell and Johnson 1989), a sign notation system that allows for both precision of sign description and predictable variation of surface forms based on grammatical detail. Empty features are used in MH notations of lexical forms, which are instantiated with spatial data during generation. The generation system is implemented as an LFG correspondence architecture (Kaplan and Bresnan 1982, Kaplan et al 1989). Correspondence functions are defined that convert an English f-structure into an ASL f-structure; build an ASL c-structure from the f-structure; and build the phonetic representation level (p-structure, where spatial and non-manual variations are revealed) from the c-structure. The concepts presented in this dissertation have been implemented in a software application, ASL Workbench. Possible future applications of this work include developing animated output, tagged corpora for linguistic analysis, and shared lexicons for gloss standardization, among others.