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Sungjin Lee

20 papers hereh-index 12471 citations30 works total

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

author position
  • first author1
  • middle author8
  • last author11

Across the 20 of 20 papers where every author was matched, so the position is known.

fields
  • cs.CL11
  • cs.LG8
  • cs.HC1
same name
  • Sungjin Lee — 14 papers, h 17
  • Sungjin Lee — 4 papers, h 0
  • Sungjin Lee — 3 papers, h 7
  • Sungjin Lee — 3 papers, h 1
  • Sungjin Lee — 2 papers, h 2
  • Sungjin Lee — 2 papers, h 15

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
20182023
most citedThe Eighth Dialog System Technology Challenge

53 citations · 78 across the 19 of their papers we have counts for

collaborators
Showing 2023Show all

4 papers · 1 filter

cs.CL2023

Data Augmentation for Improving Tail-traffic Robustness in Skill-routing for Dialogue Systems

Ting-Wei Wu, Fatemeh Sheikholeslami, Mohammad Kachuee +2

Large-scale conversational systems typically rely on a skill-routing component to route a user request to an appropriate skill and interpretation to serve the request. In such syst…

cs.CL2023★ 1 cited

Open World Classification with Adaptive Negative Samples

Ke Bai, Guoyin Wang, Jiwei Li +5

Open world classification is a task in natural language processing with key practical relevance and impact. Since the open or {\em unknown} category data only manifests in the infe…

cs.CL2023

Cluster-Guided Label Generation in Extreme Multi-Label Classification

Taehee Jung, Joo-Kyung Kim, Sungjin Lee +1

For extreme multi-label classification (XMC), existing classification-based models poorly perform for tail labels and often ignore the semantic relations among labels, like treatin…

cs.CL2023★ 1 cited

Selective In-Context Data Augmentation for Intent Detection using Pointwise V-Information

Yen-Ting Lin, Alexandros Papangelis, Seokhwan Kim +6

This work focuses on in-context data augmentation for intent detection. Having found that augmentation via in-context prompting of large pre-trained language models (PLMs) alone do…

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