We present observations from three exercises designed to map the effective listening and speaking skills of an operator of a speech-to-speech translation system (S2S) to the Interagency Language Roundtable (ILR) scale. Such a mapping is non-trivial, but will be useful for government and military decision makers in managing expectations of S2S technology. We observed domain-dependent S2S capabilities in the ILR range of Level 0+ to Level 1, and interactive text-based machine translation in the Level 3 range.

We describe a streamlined knowledge acquisition method for semi-automatically constructing knowledge bases for a Knowledge Based Machine Translation (KBMT) system. This method forms the basis of a very simple Java-based user interface that enables a language expert to build lexical and syntactic transfer knowledge bases without extensive specialized training as an MT system builder. Following [Wu 1997], we assume that the permutation of binary-branching structures is a sufficient reordering mechanism for MT. Our syntactic knowledge is based on a novel, highly constrained grammar construction environment in which the only re-ordering mechanism is the permutation of binary-branching structures (Twisted Pair Grammar). We describe preliminary results for several fully implemented components of a Hindi/Urdu to English MT prototype being built with this interface.