World's Best Scientists 2026 revealed!

D-Index & Metrics

Computer Science

D-Index
59
Citations
17120
World Ranking
3374
National Ranking
202

Lucia Specia 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 Lucia Specia 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: 315 publications — 76th percentile

76% of scientists in this discipline score the same or lower.

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

Lucia Specia 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 Lucia Specia 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: 59 D-Index — 77th percentile

77% of scientists in this discipline score the same or lower.

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

Overview

Lucia Specia is affiliated with Imperial College London in the United Kingdom. Their research focuses primarily on computer science, with substantial work in artificial intelligence. Other subfields include computer vision and pattern recognition, signal processing, information systems, and language and linguistics.

Their main topics of research cover a range of areas, including:

  • Natural language processing techniques
  • Topic modeling
  • Multimodal machine learning applications
  • Text readability and simplification
  • Advanced image and video retrieval techniques
  • Adversarial robustness in machine learning
  • Domain adaptation and few-shot learning

Lucia Specia has published extensively, with significant contributions appearing in various reputable venues. They have a notable presence in arXiv, with 28 publications. Other frequent publication venues include:

  • Machine Translation
  • Computational Linguistics
  • Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing
  • Spiral (Imperial College London)

Among their recent papers are:

  • Simultaneous machine translation with visual context, 2020, Spiral (Imperial College London)
  • Data-Driven Sentence Simplification: Survey and Benchmark, 2020, Computational Linguistics
  • Multimodal machine translation through visuals and speech, 2020, Machine Translation
  • The (Un)Suitability of Automatic Evaluation Metrics for Text Simplification, 2021, Computational Linguistics
  • Cross-lingual visual pre-training for multimodal machine translation, 2021, Digital Collections portal (Koç University)

Frequent collaborators of Lucia Specia include:

  • Yishu Miao
  • Pranava Madhyastha
  • Marina Fomicheva
  • Ozan Çağlayan
  • Francisco Guzmán

Lucia Specia's cross-disciplinary expertise integrates visual and linguistic modalities, contributing to advancements in machine translation and text simplification. Their work reflects engagement with both foundational and applied aspects of multimodal machine learning and natural language processing.

Best Publications

  • SemEval-2017 Task 1: Semantic Textual Similarity Multilingual and Crosslingual Focused Evaluation

    Daniel M. Cer;Mona T. Diab;Eneko Agirre;Iñigo Lopez-Gazpio

  • Findings of the 2014 Workshop on Statistical Machine Translation

    Ondrej Bojar;Christian Buck;Christian Federmann;Barry Haddow

  • Findings of the 2015 Workshop on Statistical Machine Translation

    Ondřej Bojar;Rajen Chatterjee;Christian Federmann;Barry Haddow

  • SemEval-2017 Task 1: Semantic Textual Similarity - Multilingual and Cross-lingual Focused Evaluation

    Daniel Cer;Mona Diab;Eneko Agirre;Iñigo Lopez-Gazpio

  • Findings of the 2012 Workshop on Statistical Machine Translation

    Chris Callison-Burch;Philipp Koehn;Christof Monz;Matt Post

  • Findings of the 2017 Conference on Machine Translation (WMT17)

    Ondřej Bojar;Rajen Chatterjee;Christian Federmann;Yvette Graham

  • Findings of the 2016 Conference on Machine Translation

    Ondˇrej Bojar;Rajen Chatterjee;Christian Federmann;Yvette Graham

  • Integrating Folksonomies with the Semantic Web

    Lucia Specia;Enrico Motta

  • Multi30K: Multilingual English-German Image Descriptions

    Desmond Elliott;Stella Frank;Khalil Sima'an;Lucia Specia

  • Findings of the 2013 Workshop on Statistical Machine Translation

    Ondřej Bojar;Christian Buck;Chris Callison-Burch;Christian Federmann

  • Estimating the Sentence-Level Quality of Machine Translation Systems

    Lucia Specia;Marco Turchi;Nicola Cancedda;Nello Cristianini

  • A Shared Task on Multimodal Machine Translation and Crosslingual Image Description

    Lucia Specia;Stella Frank;Khalil Sima'an;Desmond Elliott

  • PET: a Tool for Post-editing and Assessing Machine Translation

    Wilker Aziz;Sheila Castilho;Lucia Specia

  • QuEst - A translation quality estimation framework

    Lucia Specia;Kashif Shah;Jose G.C. de Souza;Trevor Cohn

  • Findings of the Second Shared Task on Multimodal Machine Translation and Multilingual Image Description

    Desmond Elliott;Stella Frank;Loïc Barrault;Fethi Bougares

  • How2: A Large-scale Dataset for Multimodal Language Understanding

    Ramon Sanabria;Ozan Caglayan;Shruti Palaskar;Desmond Elliott

  • Machine translation evaluation versus quality estimation

    Lucia Specia;Dhwaj Raj;Marco Turchi

  • Exploiting Objective Annotations for Minimising Translation Post-editing Effort

    Lucia Specia

  • SemEval 2016 Task 11: Complex Word Identification

    Gustavo Paetzold;Lucia Specia

  • SemEval-2012 Task 1: English Lexical Simplification

    Lucia Specia;Sujay Kumar Jauhar;Rada Mihalcea

Frequent Co-Authors

Barry Haddow
Barry Haddow University of Edinburgh
Philipp Koehn
Philipp Koehn Johns Hopkins University
Trevor Cohn
Trevor Cohn University of Melbourne
Christof Monz
Christof Monz University of Amsterdam
Mark Stevenson
Mark Stevenson University of Melbourne
Thomas Hain
Thomas Hain University of Sheffield
Ondrej Bojar
Ondrej Bojar Charles University
Marcos Zampieri
Marcos Zampieri George Mason University
Karin Verspoor
Karin Verspoor RMIT University
Enrico Motta
Enrico Motta The Open University

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