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Dongchan Kim

Amazon

6 papers hereh-index 6195 citations9 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • middle author5
  • last author1

Across the 6 of 6 papers where every author was matched, so the position is known.

fields
  • cs.CL5
  • cs.IR1
affiliations
  • Amazon
  • Georgia Institute of Technology
  • Naver Search US
  • Microsoft
  • University of Wisconsin
HomepageORCID 0000-0002-9803-9311
same name
  • Dongchan Kim — 5 papers, h 5
  • Dongchan Kim — 4 papers, h 2
  • Dongchan Kim — 2 papers, h 12
  • Dongchan Kim — 2 papers, h 1
  • Dongchan Kim — 2 papers, h 2

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182026
most citedA Scalable Neural Shortlisting-Reranking Approach for Large-Scale Domain Classification in Natural Language Understanding

3 citations · 6 across the 6 of their papers we have counts for

collaborators
Showing 2018Show all

3 papers · 1 filter

cs.CL2018

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…

cs.CL2018★ 2 cited

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…

cs.CL2018★ 3 cited

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…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.