A Call for Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems
Abstract
As more users across the world are interacting with dialog agents in
their daily life, it calls for a renewed attention to the dynamics
between research in automatic speech recognition (ASR) and natural
language understanding (NLU). We briefly review these research areas
and lay out the current relationship between them. In light of the
observations we make in this paper we argue that (1) NLU should be
congnizant of the presence of ASR models being used upstream in a
dialog system's pipeline, (2) ASR should be able to learn from errors
found in NLU, (3) there is a need for end-to-end datasets that provide
semantic annotations on spoken input, (4) there should be stronger
collaboration between ASR and NLU research communities.
their daily life, it calls for a renewed attention to the dynamics
between research in automatic speech recognition (ASR) and natural
language understanding (NLU). We briefly review these research areas
and lay out the current relationship between them. In light of the
observations we make in this paper we argue that (1) NLU should be
congnizant of the presence of ASR models being used upstream in a
dialog system's pipeline, (2) ASR should be able to learn from errors
found in NLU, (3) there is a need for end-to-end datasets that provide
semantic annotations on spoken input, (4) there should be stronger
collaboration between ASR and NLU research communities.
Research Areas
