  Analyzing how human beings resolve syntactic ambiguity has long been an issue
of interest in the field of linguistics. It is, at the same time, one of the
most challenging issues for spoken language understanding (SLU) systems as
well. As syntactic ambiguity is intertwined with issues regarding prosody and
semantics, the computational approach toward speech intention identification is
expected to benefit from the observations of the human language processing
mechanism. In this regard, we address the task with attentive recurrent neural
networks that exploit acoustic and textual features simultaneously and reveal
how the modalities interact with each other to derive sentence meaning.
Utilizing a speech corpus recorded on Korean scripts of syntactically ambiguous
utterances, we revealed that co-attention frameworks, namely multi-hop
attention and cross-attention, show significantly superior performance in
disambiguating speech intention. With further analysis, we demonstrate that the
computational models reflect the internal relationship between auditory and
linguistic processes.
