World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
33
Citations
4373
World Ranking
12722
National Ranking
118

Yun-Nung Chen publication distribution in Computer Science in 2026

The chart shows the distribution of publications by all Research.com ranked scientists in the field of Computer Science in 2026. The highlighted bar marks where Yun-Nung Chen sits on this spectrum.

32–41 publications: 7 scientists 42–51 publications: 22 scientists 52–61 publications: 82 scientists 62–71 publications: 134 scientists 72–81 publications: 249 scientists 82–91 publications: 324 scientists 92–101 publications: 421 scientists 102–111 publications: 420 scientists 112–121 publications: 497 scientists 122–131 publications: 544 scientists 132–141 publications: 555 scientists 142–151 publications: 609 scientists 152–161 publications: 559 scientists 162–171 publications: 534 scientists 172–181 publications: 556 scientists 182–191 publications: 583 scientists 192–201 publications: 519 scientists 202–211 publications: 508 scientists 212–221 publications: 490 scientists 222–231 publications: 437 scientists 232–241 publications: 423 scientists 242–251 publications: 408 scientists 252–261 publications: 377 scientists 262–271 publications: 301 scientists 272–281 publications: 335 scientists 282–291 publications: 320 scientists 292–301 publications: 293 scientists 302–311 publications: 250 scientists 312–321 publications: 238 scientists 322–331 publications: 206 scientists 332–341 publications: 209 scientists 342–351 publications: 208 scientists 352–361 publications: 162 scientists 362–371 publications: 176 scientists 372–381 publications: 127 scientists 382–391 publications: 158 scientists 392–401 publications: 128 scientists 402–411 publications: 104 scientists 412–421 publications: 94 scientists 422–431 publications: 99 scientists 432–441 publications: 83 scientists 442–451 publications: 108 scientists 452–461 publications: 73 scientists 462–471 publications: 77 scientists 472–481 publications: 69 scientists 482–491 publications: 84 scientists 492–501 publications: 62 scientists 502–511 publications: 54 scientists 512–521 publications: 57 scientists 522–531 publications: 51 scientists 532–541 publications: 51 scientists 542–551 publications: 32 scientists 552–561 publications: 38 scientists 562–571 publications: 28 scientists 572–581 publications: 43 scientists 582–591 publications: 33 scientists 592–601 publications: 41 scientists 602–611 publications: 32 scientists 612–621 publications: 28 scientists 622–631 publications: 25 scientists 632–641 publications: 27 scientists 642–651 publications: 17 scientists 652–661 publications: 20 scientists 662–671 publications: 17 scientists 672–681 publications: 15 scientists 682–691 publications: 14 scientists 692–701 publications: 21 scientists 702–711 publications: 13 scientists 712–721 publications: 12 scientists 722–731 publications: 19 scientists 732–741 publications: 14 scientists 742–751 publications: 12 scientists 752–761 publications: 10 scientists 762–771 publications: 10 scientists 772–781 publications: 11 scientists 782–791 publications: 10 scientists 792–801 publications: 11 scientists 802–811 publications: 8 scientists 812–821 publications: 8 scientists 822–831 publications: 7 scientists 832–841 publications: 11 scientists 842–851 publications: 10 scientists 852–861 publications: 5 scientists 862–871 publications: 9 scientists 872–881 publications: 4 scientists 882–891 publications: 6 scientists 892–901 publications: 3 scientists 902–911 publications: 6 scientists 912–921 publications: 3 scientists 922–931 publications: 2 scientists 932–941 publications: 2 scientists 942–951 publications: 2 scientists 952–961 publications: 3 scientists 962–971 publications: 3 scientists 972–981 publications: 3 scientists 982–990 publications: 5 scientists 991+ publications: 100 scientists
32 publications 991+

This scientist: 184 publications — 40th percentile

40% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 991 publications or more.

Yun-Nung Chen D-index placement in Computer Science in 2026

The chart shows the D-index (discipline H-index) distribution of Computer Science scientists ranked by Research.com in 2026. The highlighted bar marks where Yun-Nung Chen sits on this spectrum.

30–31 D-Index: 879 scientists 32–33 D-Index: 983 scientists 34–35 D-Index: 918 scientists 36–37 D-Index: 990 scientists 38–39 D-Index: 968 scientists 40–41 D-Index: 907 scientists 42–43 D-Index: 821 scientists 44–45 D-Index: 763 scientists 46–47 D-Index: 689 scientists 48–49 D-Index: 543 scientists 50–51 D-Index: 543 scientists 52–53 D-Index: 518 scientists 54–55 D-Index: 500 scientists 56–57 D-Index: 458 scientists 58–59 D-Index: 400 scientists 60–61 D-Index: 337 scientists 62–63 D-Index: 308 scientists 64–65 D-Index: 292 scientists 66–67 D-Index: 249 scientists 68–69 D-Index: 213 scientists 70–71 D-Index: 192 scientists 72–73 D-Index: 189 scientists 74–75 D-Index: 165 scientists 76–77 D-Index: 139 scientists 78–79 D-Index: 119 scientists 80–81 D-Index: 121 scientists 82–83 D-Index: 113 scientists 84–85 D-Index: 88 scientists 86–87 D-Index: 87 scientists 88–89 D-Index: 75 scientists 90–91 D-Index: 69 scientists 92–93 D-Index: 57 scientists 94–95 D-Index: 46 scientists 96–97 D-Index: 38 scientists 98–99 D-Index: 34 scientists 100–101 D-Index: 36 scientists 102–103 D-Index: 27 scientists 104–105 D-Index: 37 scientists 106–107 D-Index: 18 scientists 108–109 D-Index: 31 scientists 110–111 D-Index: 19 scientists 112–113 D-Index: 16 scientists 114–115 D-Index: 12 scientists 116–117 D-Index: 20 scientists 118–119 D-Index: 15 scientists 120–121 D-Index: 5 scientists 122–123 D-Index: 20 scientists 124–125 D-Index: 8 scientists 126–127 D-Index: 5 scientists 128–129 D-Index: 7 scientists 130 D-Index: 3 scientists 131+ D-Index: 98 scientists
30 D-Index 131+

This scientist: 33 D-Index — 13th percentile

13% of scientists in this discipline score the same or lower.

The last bar groups every scientist with 131 D-Index or more.

Overview

Yun-Nung Chen is affiliated with National Taiwan University in Taiwan and has contributed extensively to the field of computer science, with a focus on artificial intelligence and related subfields. Their scholarly work spans Natural Language Processing, speech and dialogue systems, vision, and atmospheric science, among other technical areas.

The main fields of study in Chen's research portfolio include:

  • Computer Science

Within computer science, their work predominantly covers these subfields:

  • Artificial Intelligence
  • Computer Vision and Pattern Recognition
  • Atmospheric Science
  • Information Systems
  • General Social Sciences

The primary research topics addressed by Chen include:

  • Natural Language Processing Techniques
  • Topic Modeling
  • Speech and dialogue systems
  • Multimodal Machine Learning Applications
  • Speech Recognition and Synthesis
  • Text and Document Classification Technologies
  • Domain Adaptation and Few-Shot Learning

Chen has published a significant number of papers, with notable recent works including:

  • Overview of the Ninth Dialog System Technology Challenge: DSTC9, 2020, arXiv (Cornell University)
  • The Prevalence and Impact of Fake News on COVID-19 Vaccination in Taiwan: Retrospective Study of Digital Media, 2022, Journal of Medical Internet Research
  • Overview of the Ninth Dialog System Technology Challenge: DSTC9, 2024, IEEE/ACM Transactions on Audio Speech and Language Processing
  • Knowledge-Grounded Response Generation with Deep Attentional Latent-Variable Model, 2020, Computer Speech & Language
  • Real-time Tropical Cyclone Intensity Estimation by Handling Temporally Heterogeneous Satellite Data, 2021, Proceedings of the AAAI Conference on Artificial Intelligence

The venues where Chen's research is frequently published include:

  • arXiv (Cornell University)
  • IEEE/ACM Transactions on Audio Speech and Language Processing
  • Computer Speech & Language
  • Proceedings of the AAAI Conference on Artificial Intelligence
  • Journal of Medical Internet Research

Collaborations are an important aspect of Chen's academic work. Frequent coauthors include:

  • Chao-Wei Huang
  • Shang-Yu Su
  • Hung-yi Lee
  • Ting-Rui Chiang

Best Publications

  • Multi-Domain Joint Semantic Frame Parsing Using Bi-Directional RNN-LSTM.

    Dilek Hakkani-Tür;Gokhan Tur;Asli Celikyilmaz;Yun-Nung Chen

  • Slot-gated modeling for joint slot filling and intent prediction

    Chih Wen Goo;Guang Gao;Yun Kai Hsu;Chih Li Huo

  • Towards End-to-End Reinforcement Learning of Dialogue Agents for Information Access

    Bhuwan Dhingra;Lihong Li;Xiujun Li;Jianfeng Gao

  • End-to-End Task-Completion Neural Dialogue Systems

    Xiujun Li;Yun-Nung Chen;Lihong Li;Jianfeng Gao

  • A User Simulator for Task-Completion Dialogues

    Xiujun Li;Zachary C. Lipton;Bhuwan Dhingra;Lihong Li

  • End-to-End Memory Networks with Knowledge Carryover for Multi-Turn Spoken Language Understanding.

    Yun-Nung Chen;Dilek Hakkani-Tür;Gokhan Tur;Jianfeng Gao

  • Tree transformer: Integrating tree structures into self-attention

    Yaushian Wang;Hung-Yi Lee;Yun-Nung Chen

  • Unsupervised induction and filling of semantic slots for spoken dialogue systems using frame-semantic parsing

    Yun-Nung Chen;William Yang Wang;Alexander I. Rudnicky

  • Semantically-Aligned Equation Generation for Solving and Reasoning Math Word Problems

    Ting-Rui Chiang;Yun-Nung Chen

  • Abstractive Dialogue Summarization with Sentence-Gated Modeling Optimized by Dialogue Acts

    Chih-Wen Goo;Yun-Nung Chen

  • DyKgChat: Benchmarking Dialogue Generation Grounding on Dynamic Knowledge Graphs

    Yi-Lin Tuan;Yun-Nung Chen;Hung-yi Lee

  • End-to-end joint learning of natural language understanding and dialogue manager

    Xuesong Yang;Yun-Nung Chen;Dilek Hakkani-Tur;Paul Crook

  • Zero-shot learning of intent embeddings for expansion by convolutional deep structured semantic models

    Yun-Nung Chen;Dilek Hakkani-Tur;Xiaodong He

  • Discriminative Deep Dyna-Q: Robust Planning for Dialogue Policy Learning

    Shang-Yu Su;Xiujun Li;Jianfeng Gao;Jingjing Liu

  • Detecting Actionable Items in a Conversation among Participants

    Dilek Zeynep Hakkani-Tur;Xiaodong He;Yun-Nung Chen

  • What Do Position Embeddings Learn? An Empirical Study of Pre-Trained Language Model Positional Encoding

    Yu-An Wang;Yun-Nung Chen

  • Adversarial Advantage Actor-Critic Model for Task-Completion Dialogue Policy Learning

    Baolin Peng;Xiujun Li;Jianfeng Gao;Jingjing Liu

  • PLM-ICD: Automatic ICD Coding with Pretrained Language Models

    Unknown

  • LLM-Eval: Unified Multi-Dimensional Automatic Evaluation for Open-Domain Conversations with Large Language Models

    Unknown

  • Syntax or semantics? knowledge-guided joint semantic frame parsing

    Yun-Nung Chen;Dilek Hakanni-Tur;Gokhan Tur;Asli Celikyilmaz

  • Deep Learning for Dialogue Systems

    Yun-Nung Chen;Asli Celikyilmaz;Dilek Hakkani-Tur

  • How time matters: Learning time-decay attention for contextual spoken language understanding in dialogues

    Shang-Yu Su;Pei-Chieh Yuan;Yun-Nung Chen

  • Overview of the Ninth Dialog System Technology Challenge: DSTC9

    Chulaka Gunasekara;Seokhwan Kim;Luis Fernando D'Haro;Abhinav Rastogi

Frequent Co-Authors

Jianfeng Gao
Jianfeng Gao Microsoft (United States)
Alexander I. Rudnicky
Alexander I. Rudnicky Carnegie Mellon University
Hung-yi Lee
Hung-yi Lee National Taiwan University
Dilek Hakkani-Tur
Dilek Hakkani-Tur University of Illinois at Urbana-Champaign
Li Deng
Li Deng Citadel
Asli Celikyilmaz
Asli Celikyilmaz Facebook (United States)
Lin-Shan Lee
Lin-Shan Lee National Taiwan University
Gokhan Tur
Gokhan Tur Amazon (United States)
William Yang Wang
William Yang Wang University of California, Santa Barbara
Florian Metze
Florian Metze Carnegie Mellon University

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