Invited talk: Efficient HPSG Realization for Precision Machine Translation

Abstract

I will review recent advances in grammar-based sentence realization from logical-form meaning representations. The LOGON MT prototype aims at the fully-automated, highquality translation of Norwegian instructional texts (on backcountry activities) into English. The LOGON generator operates off underspecified meaning representations derived from ‘deep’ grammatical analysis (in the LFG framework) and subsequent semantic transfer. The generator builds on the LinGO English Resource Grammar (in the HPSG framework) and combines a highly optimized chart-based algorithm with a rich, probabilistic model to rank alternate realizations. Integration of the stochastic model into the enumeration of outputs from the packed chart allows the generator to selectively unpack n-best lists of realizations with minimal search. Besides empirical results for the realization task when evaluated in isolation, I will present a summary of quantitative measures on the current development status (and promise) of the LOGON MT pipeline as a whole.