Spoken Language Understanding in dialogue systems, using a 2-layer Markov Logic Network: improving semantic accuracy
Ivan Meza-Ruiz
and
Sebastian Riedel
and
Oliver Lemon
Abstract:
We describe a two layer Markov Logic Network
(MLN) model for the Spoken Language
Understanding (SLU) task in dialogue
systems. We augment the set of features
used in Meza-Ruiz et al. (2008) with the
help of off-the-shelf resources. We show
that this setup increases the performance of
the previous MLN models, which also outperform
the state-of-the art .Hidden Vector
State. (HVS) model of He and Young 2006.
In particular the 2 layer approach produces
more accurate sets of slot-values for user utterances
(9% improvement).
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