The full paper explores the possibility of using Subsequential
Transducers (SST), a
finite state model, in limited domain translation tasks, both for text
and speech input.
A distinctive advantage of SSTs is that they can be efficiently learned
from sets of
input-output examples by means of OSTIA, the Onward Subsequential Transducer
Inference Algorithm (Oncina et al. 1993). In this work a
technique is proposed to
increase the performance of OSTIA by reducing the asynchrony between the
input
and output sentences, the use of error correcting parsing to increase
the robustness of the models is explored, and an integrated architecture
for
speech input translation by means of SSTs is described.