Learning consistent semantics from training data

Previously (see ICASSP-93, vol.2, p.55, 1993 and Eurospeech 93, vol.2, p.1331, 1993) we described a speech understanding system called "CHANEL" with two components: a chart-based parser that analyzes semantically important word islands within an utterance; and a component based on "semantic classification trees" (SCTs) that builds the representation for the complete utterance. The construction of a natural-language understanding (NLU) system is a task that has traditionally required lavish expenditure of programmer-hours. By dividing the task in this way, we enabled many of the system's rules (those contained in the SCT component) to be learned automatically from training data, freeing human expertise to be applied where it is most effective. This paper describes recent improvements to both components of CHANEL, along with a new module that handles context-dependent utterances. The new version of CHANEL has a new use for SCTs: a special SCT decides whether a sentence is context-dependent or not.<<ETX>>