Accurate Statistical Spoken Language Understanding from Limited Development Resources
Ivan Meza-Ruiz
and
Sebastian Riedel
and
Oliver Lemon
Abstract:
Robust Spoken Language Understanding (SLU) is a key
component of spoken dialogue systems. Recent statistical
approaches to this problem require additional resources (e.g.
gazetteers, grammars, syntactic treebanks) which are expensive
and time-consuming to produce and maintain. However,
simple datasets annotated only with slot-values are commonly
used in dialogue systems development, and are easy to collect,
automatically annotate, and update. We show that it is possible
to reach state-of-the-art performance using minimal additional
resources, by using Markov Logic Networks (MLNs).
We also show that performance can be further improved by
exploiting long distance dependencies between slot-values.
For example, by representing such features inMLNs, butwithout
using a gazetteer, we outperform the Hidden Vector State
(HVS) model of He and Young 2006 (1.26% improvement, a
13% error reduction).
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