  To interpret natural language at the discourse level, it is very useful to
accurately recognize dialogue acts, such as SUGGEST, in identifying speaker
intentions. Our research explores the utility of a machine learning method
called Transformation-Based Learning (TBL) in computing dialogue acts, because
TBL has a number of advantages over alternative approaches for this
application. We have identified some extensions to TBL that are necessary in
order to address the limitations of the original algorithm and the particular
demands of discourse processing. We use a Monte Carlo strategy to increase the
applicability of the TBL method, and we select features of utterances that can
be used as input to improve the performance of TBL. Our system is currently
being tested on the VerbMobil corpora of spoken dialogues, producing promising
preliminary results.
