Revisiting the Boundary between ASR and NLU in the Age of Conversational Dialog Systems
This addresses the need for better speech understanding for users interacting with dialog agents, but it is incremental as it revisits existing boundaries without proposing new methods.
The paper tackles the problem of improving speech understanding in conversational dialog systems by examining the relationship between automatic speech recognition (ASR) and natural language understanding (NLU), arguing for greater integration and collaboration between these fields.
As more users across the world are interacting with dialog agents in their daily life, there is a need for better speech understanding that calls for 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 cognizant 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.