Machine Learning for
Ancient Languages

ACL 2024 Workshop
15 August 2024 | Hybrid in Bangkok, Thailand & remote

logoThe Workshop

The ML4AL Workshop aims to inspire collaboration and support research momentum in the emerging field of Machine Learning for the study of ancient texts.

Ancient languages preserve the cultures and histories of the past. However, their study is fraught with difficulties, and experts must tackle a range of challenging text-based tasks, from deciphering lost languages to restoring damaged inscriptions, to determining the authorship of works of literature.

Technological aids have long supported the study of ancient texts, but in recent years advances in Artificial Intelligence and Machine Learning have enabled analyses on ancient languages on an unprecedented scale and in unparalleled detail.

The ML4AL workshop will showcase the scientific opportunities at the intersection of the Humanities and ML, and spotlight promising directions for future endeavours within this rising field.

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    When

    15 August 2024.

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    Where

    Hybrid format: Bangkok (Thailand) and online.

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    Registration

    Refer to the official ACL 2024 website.

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Topics of interest


cuneiform inscription

  • Digitization: bringing textual sources to a high-quality machine-readable format.
  • Restoration: recovering missing text and reassembling fragmented written artifacts.
  • Attribution: contextualising a document within its original geographical, chronological and authorial setting.
  • Linguistic analysis: involving linguistic tasks such as semantic analysis, part of speech (POS) tagging, text parsing and segmentation.
  • Textual criticism: the process of reconstructing a text’s philological tradition of textual transmission.
  • Translation and decipherment: which aim to make a text’s language comprehensible and interpretable to modern-day researchers.
We particularly encourage submissions which tackle low-data, underrepresented, non-Western ancient languages.

Scope

ML4AL is designed to facilitate and invigorate the ongoing collaborative momentum between ML and the Humanities, to foster a deeper understanding of our past.
We invite contributions tackling texts from the diverse corners of the globe, in any language, script or medium. We establish a chronological scope from the inception of writing systems in ancient Mesopotamia and Egypt (3400 BCE) to the late first millennium CE.
We welcome long (8 page) and short (4 page) paper submissions, on OpenReview or ARR: see our Submission page for more information.
Accepted regular workshop papers will be included in the workshop proceedings, but non-archival submissions are also welcome.

Objectives

An inclusive scope

By showcasing the scientific opportunities at the intersection of the Humanities and ML, the workshop offers a roadmap for this burgeoning interdisciplinary field.

A collaborative mindset

ML4AL emphasises the value and urgency of active collaboration between the specialists from both fields, to produce compelling and consequential research.

An explainable approach

The workshop aims to generate awareness of the risk posed by data bias and digital colonialism, emphasise the importance of standardised datasets, metrics and benchmarks, and encourage the development of explainable AI tools.

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Important dates

All deadlines are 11:59 pm UTC -12h (“anywhere on Earth”).

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    May 17, 2024

    Direct paper submission deadline

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    June 17, 2024

    Notification of acceptance

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    July 1, 2024

    Camera-ready paper due

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    August 15, 2024

    Workshop

roman inscription


Organising Committee

  • Dr John Pavlopoulos, Athens University of Economics and Business, Greece.
  • Dr Thea Sommerschield, University of Nottingham, UK.
  • Dr Yannis Assael, Google DeepMind, UK.
  • Dr Shai Gordin, Ariel University, Israel.
  • Prof. Kyunghyun Cho, NYU, CIFAR, Genentech, USA.
  • Prof. Marco Passarotti, Università Cattolica del Sacro Cuore, Italy.
  • Dr Rachele Sprugnoli, Università di Parma, Italy.
  • Dr Yudong Liu, Western Washington University, USA.
  • Dr Bin Li, Nanjing Normal University, China.
  • Dr Adam Anderson, UC Berkeley, USA.


Program Committee

  • Masayuki Asahara, National Institute for Japanese and Linguistics, Japan.
  • John Bodel, Brown University, USA.
  • Kyunghyun Cho, New York University, USA.
  • Gregory Crane, Tufts University, USA.
  • Katrien De Graef, Ghent University, Belgium.
  • Sanhong Deng, Nanjing University, China.
  • Mark Depauw, KU Leuven, Belgium.
  • Hanne Eckhoff, University of Oxford, UK.
  • Margherita Fantoli, KU Leuven, Belgium.
  • Minxuan Feng, Nanjing University, China.
  • Ethan Fetaya, Bar-Ilan University, Israel.
  • Federica Gamba, Charles University, Czech Republic.
  • Laura Hawkins, Harvard University, USA.
  • Chul Heo, Pusan University, Republic of Korea.
  • Petra Heřmánková, Aarhus University, Denmark & Johannes Gutenberg-Universität Mainz, Germany.
  • Marietta Horster, Johannes Gutenberg-Universität Mainz, Germany.
  • Renfen Hu, Beijing University, China.
  • Kyle Johnson, TikTok, USA.
  • Alek Keersmaekers, KU Leuven, Belgium.
  • Ussen Kimanuka, Pan African University Institute, Kenya.
  • Thomas Koentges, You Say Data Limited, New Zealand.
  • Els Lefever, Ghent University, Belgium.
  • Chaya Liebeskind, Jerusalem College of Technology, Israel.
  • Eliese-Sophia Lincke, Freie Universität Berlin, Germany.
  • Chao-Lin Liu, Chengchi University, Taiwan.
  • Liu Liu, Nanjing University, China.
  • Congjun Long, Chinese Academy of Social Sciences, China.
  • Jiaming Luo, Google Canada, Canada.
  • Massimo Maiocchi, Ca' Foscari University of Venice, Italy.
  • Isabelle Marthot-Santaniello, University of Basel, Switzerland.
  • Barbara McGillivray, King's College London, UK.
  • M. Willis Monroe, University of New Brunswick, Canada.
  • Alex Mullen, University of Nottingham, UK.
  • Chiara Palladino, Furman University, USA.
  • Chanjun Park, Upstage, Republic of Korea.
  • Edoardo M. Ponti, University of Edinburgh, UK.
  • Mladen Popovic, University of Groningen, The Netherlands.
  • Jonathan Prag, University of Oxford, UK.
  • Avital Romach, Yale University, USA.
  • Edgar Roman-Rangel, Instituto Tecnológico Autónomo de México, Mexico.
  • Matteo Romanello, University of Lausanne, Switzerland.
  • Brent Seales, University of Kentucky, USA.
  • Andrew Senior, Google DeepMind, UK.
  • Si Shen, Nanjing University, China.
  • Barak Sober, The Hebrew University of Jerusalem, Israel.
  • Richard Sproat, Google DeepMind, Japan.
  • Gabriel Stanovsky, The Hebrew University of Jerusalem, Israel.
  • Vanessa Stefanak, Google DeepMind, UK.
  • Silvia Stopponi, University of Groningen, The Netherlands.
  • Qi Su, Peking University, China.
  • Matthew I. Swindall, Middle Tennessee State University, USA.
  • Xuri Tang, Huazhong University, China.
  • Charlotte Tupman, University of Exeter, UK.
  • Dongbo Wang, Nanjing University, China.
  • Haneul Yoo, KAIST, Republic of Korea.
  • Chongsheng Zhang, Henan University, China.


Sponsors & Support

    Supporting Organisation

    ARCHIMEDES Unit - Research on artificial intelligence, data science and algorithms


    athena_archimedes