tailieunhanh - Báo cáo khoa học: "Re-Usable Tools for Precision Machine Translation∗"

The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality. The demonstrator embodies an interesting combination of hand-built, symbolic resources and stochastic processes. h1 , { h1 :proposition m(h3 ), h4 :proper q(x5 , h6 , h7 ), h8 :named(x5,‘Bodø’), h9 : populate v(e2 , , x5 ), h9 : densely r(e2 ) }, { h 3 =q h9 , h6 =q h8 } Figure 1: Simplified MRS representation for the utterance ‘Bodø is densely populated.’ The core of the structure is a bag of elementary predications (EPs), using distinguished. | Re-Usable Tools for Precision Machine Translation Jan Tore L0nning and Stephan Oepen Universitetet i Oslo Computer Science Institute Boks 1080 Blindern 0316 Oslo Norway Center for the Study of Language and Information Stanford CA 94305 USA jtl@ oe@ Abstract The LOGON MT demonstrator assembles independently valuable general-purpose NLP components into a machine translation pipeline that capitalizes on output quality. The demonstrator embodies an interesting combination of hand-built symbolic resources and stochastic processes. 1 Background The LOGON projects aims at building an experimental machine translation system from Norwegian to English of texts in the domain of hiking in the wilderness Oepen et al. 2004 . It is funded within the Norwegian Research Council program for building national infrastructure for language technology Fenstad et al. 2006 . It is the goal for the program as well as for the project to include various areas of language technology as well as various methods in particular symbolic and empirical methods. Besides the project aims at reusing available resources and in turn producing re-usable technology. In spite of significant progress in statistical approaches to machine translation we doubt the long-term value of pure statistical or data-driven approaches both practically and scientifically. To ensure grammaticality of outputs as well as felicity of the translation both linguistic grammars and deep semantic analysis are needed. The architecture of the LOGON system hence consists of a symbolic backbone system combined with various stochastic components for ranking system hypotheses. In a nutshell a central research question in LOGON is to what degree state-of-the-art deep NLP resources can contribute towards a precision MT system. We hope to engage the conference audience in some reflection on this question by means of the interactive presentation. 2 System Design The backbone of the LOGON prototype implements a .