@ COLING 2027 (May 9–14, 2027)
Macau, China
NEWS
LoResMT 2027 (https://www.loresmt.org/) confirmed to be held at COLING 2027 (https://2027.coling-iccl.org/).
Further information will follow as soon as it is available.
TIMELINE, temporary
Submission deadline: December 26, 2027 (Anywhere on Earth)
Pre-reviewed (ARR) submission deadline: February 15, 2027
Notification of acceptance: February 30, 2027
Camera-ready paper due: March 10, 2027
Pre-recorded video due (hard deadline): March 24, 2027
Workshop date: to be confirmed with COLING 2027
COLING 2027 Main Conference: May 9–14, 2027
SCOPE
Based on the success of past low-resource machine translation (MT) workshops at AMTA 2018, MT Summit 2019, AACL-IJCNLP 2020, AMTA 2021, COLING 2022, EACL 2023, ACL 2024, NAACL 2025, EACL 2026, we introduce LoResMT 2027 workshop at COLING 2027. In the past few years, machine translation (MT) performance has been improved significantly. With the development of new techniques such as multilingual translation and transfer learning, the use of MT is no longer a privilege for users of popular languages. Consequently, there has been an increasing interest in the community to expand the coverage to more languages with different geographical presences, degrees of diffusion and digitalization. However, the goal to increase MT coverage for more users speaking diverse languages is limited by the fact the MT methods demand huge amounts of data to train quality systems, which has posed a major obstacle to develop MT systems for low-resource languages. Therefore, developing comparable MT systems with relatively small datasets is still highly desirable.
In addition, despite the fast developments of MT technologies, MT systems still rely on several NLP tools to pre-process human-generated texts in the forms that are required as input for MT systems and post-process the MT output in proper textual forms in the target language. This is especially true when it comes to systems involving low-resource languages. These NLP tools include, but are not limited to, several kinds of word tokenizers/de-tokenizers, word segmenters, morphology analysers, etc. The performance of these tools has a great impact on the quality of the resulting translation. There is only limited discussion on these NLP tools, their methods, their role in training different MT systems, and their coverage of support in the many languages of the world.
The workshop provides a discussion panel for researchers working on MT systems/methods for low-resource and under-represented languages in general. We would like to help review/overview the state of MT for low-resource languages and define the most important directions. We also solicit papers dedicated to supplementary NLP tools that are used in any language and especially in low-resource languages. Overview papers of these NLP tools are very welcome. It will be beneficial if the evaluations of these tools in research papers include their impact on the quality of MT output.
TOPICS
We are highly interested in (1) original research papers, (2) review/opinion papers, and (3) online systems on the topics below; however, we welcome all novel ideas that cover research on low-resource languages.
- COVID-related corpora, their translations and corresponding NLP/MT systems
- Neural machine translation for low-resource languages
- Work that presents online systems for practical use by native speakers
- Word tokenizers/de-tokenizers for specific languages
- Word/morpheme segmenters for specific languages
- Alignment/Re-ordering tools for specific language pairs
- Use of morphology analyzers and/or morpheme segmenters in MT
- Multilingual/cross-lingual NLP tools for MT
- Corpora creation and curation technologies for low-resource languages
- Review of available parallel corpora for low-resource languages
- Research and review papers of MT methods for low-resource languages
- MT systems/methods (e.g. rule-based, SMT, NMT) for low-resource languages
- Pivot MT for low-resource languages
- Zero-shot MT for low-resource languages
- Fast building of MT systems for low-resource languages
- Re-usability of existing MT systems for low-resource languages
- Machine translation for language preservation
SUBMISSION INFORMATION
We are soliciting two types of submissions: (1) research, review, and position papers and (2) system demonstration papers. For research, review and position papers, the length of each paper should be at least four (4) and not exceed eight (8) pages, plus unlimited pages for references. For system demonstration papers, the limit is four (4) pages. Submissions should be formatted according to the official ACL style templates (Overleaf). Please refer to EACL submission guideline for further information. Accepted papers will be published online in the EACL 2026 proceedings and will be presented at the conference.
Submissions must be anonymized and should be done using the provided submission system. Scientific papers that have been or will be submitted to other venues must be declared as such and must be withdrawn from the other venues if accepted and published at LoResMT. The review will be double-blind. Authors of an accepted paper should present their paper in person or virtually at EACL 2026. Papers should be submitted in PDF to the LoResMT Open Review.
We would like to encourage authors to cite papers written in ANY language that are related to the topics, as long as both original bibliographic items and their corresponding English translations are provided.
Registration is handled by the main conference (https://2026.eacl.org/registration).
Turkic languages translation challenge @ LoResMT'2026
We invite MT & low-resource NLP teams to a new shared task on translating Turkic languages under realistic low-data conditions.
Language Pairs:
Russian-Bashkir
English-Chuvash
Russian-Kazakh
English-Tatar
We are also going to add Russian-Kyrgyz language pair shortly. Turkic languages are morphology-rich, dialectally diverse, under-served in MT. This task targets real impact: cultural representation while advancing transfer learning and morphology-aware models.
Data
We provide test data only, while you can use any publicly available data for training.
Evaluation: chrF++
Key dates
Evaluation: Dec 1, 2025 - Jan 11, 2026
System description due: Jan 27, 2026
Ready to join?
ORGANIZING COMMITTEE (LISTED ALPHABETICALLY)
Atul Kr. Ojha
Chao-Hong Liu
Ekaterina Vylomova
Flammie Pirinen
Jonathan Washington
Nathaniel Oco
Xiaobing Zhao
PROGRAM COMMITTEE (LISTED ALPHABETICALLY)
Abigail Walsh, ADAPT Centre, Dublin City University, Ireland
Alberto Poncelas, Rakuten, Singapore
Ali Hatami, University of Galway
Alina Karakanta, Leiden University
Amirhossein Tebbifakhr, Fondazione Bruno Kessler
Anna Currey, Amazon Web Services
Aswarth Abhilash Dara, Apple
Arturo Oncevay, University of Edinburgh
Atul Kr. Ojha, DSI, University of Galway
Barry Haddow, University of Edinburgh
Bogdan Babych, Heidelberg University
Chao-Hong Liu, ITRI, Potamu Research
Constantine Lignos, Brandeis University, USA
Daan van Esch, Google
Diptesh Kanojia, University of Surrey, UK
Duygu Ataman, University of Zurich
Ekaterina Vylomova, University of Melbourne, Australia
Eleni Metheniti, CLLE-CNRS and IRIT-CNRS
Flammie Pirinen, UiT The Arctic University of Norway, Tromsø
Koel Dutta Chowdhury, Saarland University (Germany)
Jade Abbott, Retro Rabbit
Jasper Kyle Catapang, University of the Philippines
Jinliang Lu, Institute of automation, Chinese Academy of Sciences
John P. McCrae, DSI, University of Galway
Liangyou Li, Noah’s Ark Lab, Huawei Technologies
Majid Latifi, University of York, York, UK
Maria Art Antonette Clariño, University of the Philippines Los Baños
Mathias Müller, University of Zurich
Milind Agarwal, George Mason University
Nathaniel Oco, De La Salle University (Philippines)
Pavel Rychlý, Masaryk University
Pengwei Li, Meta
Rico Sennrich, University of Zurich
Saliha Muradoglu, The Australian National University
Sangjee Dondrub, Qinghai Normal University
Santanu Pal, WIPRO AI
Sardana Ivanova, University of Helsinki
Sourabrata Mukherjee, Charles University
Surafel Melaku Lakew, Amazon AI
Surangika Ranathunga, Massey University
Thepchai Supnithi, National Electronics and Computer Technology Center
Timothee Mickus, University of Helsinki
Wen Lai, Center for Information and Language Processing, LMU Munich
Xuebo Liu, Harbin Institute of Technolgy, Shenzhen
Valentin Malykh, MTS AI / ITMO University
Yalemisew Abgaz, Dublin City University
Yasmin Moslem, ADAPT Centre, TCD, Ireland
Zhanibek Kozhirbayev, National Laboratory Astana, Nazarbayev University
CONTACT
Please email loresmt@googlegroups.com if you have any questions/comments/suggestions.
SHARED TASK DATA
PROCEEDINGS
Proceedings of LoResMT 2026 Workshop
https://aclanthology.org/volumes/2026.loresmt-1/
Proceedings of LoResMT 2025 Workshop
https://aclanthology.org/volumes/2025.loresmt-1/
Proceedings of LoResMT 2024 Workshop
https://aclanthology.org/volumes/2024.loresmt-1/
https://aclanthology.org/events/eacl-2023/#2023loresmt-1
https://aclanthology.org/volumes/2022.loresmt-1/
https://aclanthology.org/volumes/2021.mtsummit-loresmt/
https://aclanthology.org/volumes/2020.loresmt-1/
https://aclanthology.org/volumes/W19-68/