A Joint Model for Discovery of Aspects in Utterances

We describe a joint model for understanding
user actions in natural language utterances.
Our multi-layer generative approach uses both
labeled and unlabeled utterances to jointly
learn aspects regarding utterance’s target domain
(e.g. movies), intention (e.g., finding a
movie) along with other semantic units (e.g.,
movie name). We inject information extracted
from unstructured web search query logs as
prior information to enhance the generative
process of the natural language utterance understanding
model. Using utterances from five
domains, our approach shows up to 4.5% improvement
on domain and dialog act performance
over cascaded approach in which each
semantic component is learned sequentially
and a supervised joint learning model (which
requires fully labeled data).