9 citations · 35 across the 19 of their papers we have counts for
6 papers · 1 filter
Supervised Domain Enablement Attention for Personalized Domain Classification
Joo-Kyung Kim, Young-Bum Kim
In large-scale domain classification for natural language understanding, leveraging each user's domain enablement information, which refers to the preferred or authenticated domain…
Coupled Representation Learning for Domains, Intents and Slots in Spoken Language Understanding
JIhwan Lee, Dongchan Kim, Ruhi Sarikaya +1
Representation learning is an essential problem in a wide range of applications and it is important for performing downstream tasks successfully. In this paper, we propose a new mo…
Joint Learning of Domain Classification and Out-of-Domain Detection with Dynamic Class Weighting for Satisficing False Acceptance Rates
Joo-Kyung Kim, Young-Bum Kim
In domain classification for spoken dialog systems, correct detection of out-of-domain (OOD) utterances is crucial because it reduces confusion and unnecessary interaction costs be…
Efficient Large-Scale Domain Classification with Personalized Attention
Young-Bum Kim, Dongchan Kim, Anjishnu Kumar +1
In this paper, we explore the task of mapping spoken language utterances to one of thousands of natural language understanding domains in intelligent personal digital assistants (I…
A Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding
Young-Bum Kim, Dongchan Kim, Joo-Kyung Kim +1
Intelligent personal digital assistants (IPDAs), a popular real-life application with spoken language understanding capabilities, can cover potentially thousands of overlapping dom…
OneNet: Joint Domain, Intent, Slot Prediction for Spoken Language Understanding
Young-Bum Kim, Sungjin Lee, Karl Stratos
In practice, most spoken language understanding systems process user input in a pipelined manner; first domain is predicted, then intent and semantic slots are inferred according t…