The Fifth Workshop on Technologies for Machine Translation of

Low-Resource Languages (LoResMT 2022)

@ COLING 2022 (October 12-17, 2022)

Gyeongju, Republic of Korea, October 16, 2022

NEWS

TIMELINE

  • Papers Due: July 18, 2022 (Monday)

  • Notification of Acceptance: August 22, 2022 (Monday)

  • Camera-ready papers due: September 5, 2022 (Monday)

  • LoResMT workshop: October 16, 2022 (Sunday whole day)

  • COLING Conference: October 12-17, 2022

SCOPE


Based on the success of past low-resource machine translation (MT) workshops at AMTA 2018 (https://amtaweb.org/), MT Summit 2019 (https://www.mtsummit2019.com), AACL-IJCNLP 2020 (http://aacl2020.org/), and AMTA 2021, we introduce the Fifth LoResMT workshop at COLING 2022. 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 COLING 2022 style templates (LaTeX, Word, Overleaf). Accepted papers will be published online in the COLING 2022 proceedings and will be presented at the conference.


Submissions must be anonymized and should be done using the official conference management system (https://www.softconf.com/coling2022/LoResMT_2022/). 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.


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://coling2022.org/coling).

ORGANIZING COMMITTEE (LISTED ALPHABETICALLY)


Atul Kr. Ojha, DSI, National University of Ireland Galway & Panlingua Language Processing LLP

Chao-Hong Liu, Potamu Research Ltd

Ekaterina Vylomova, University of Melbourne, Australia

Jade Abbott, Retro Rabbit

Jonathan Washington, Swarthmore College

Nathaniel Oco, National University (Philippines)

Tommi A Pirinen, UiT The Arctic University of Norway, Tromsø

Valentin Malykh, Huawei Noah’s Ark lab and Kazan Federal University

Varvara Logacheva, Skolkovo Institute of Science and Technology

Xiaobing Zhao, Minzu University of China

PROGRAM COMMITTEE (LISTED ALPHABETICALLY)


Alberto Poncelas, Rakuten, Singapore

Alina Karakanta, Fondazione Bruno Kessler

Amirhossein Tebbifakhr, Fondazione Bruno Kessler

Anna Currey, Amazon Web Services

Aswarth Abhilash Dara, Amazon

Arturo Oncevay, University of Edinburgh

Atul Kr. Ojha, DSI, National University of Ireland Galway & Panlingua Language Processing LLP

Bharathi Raja Chakravarthi, DSI, National University of Ireland Galway

Bogdan Babych, Heidelberg University

Chao-Hong Liu, Potamu Research Ltd

Duygu Ataman, University of Zurich

Ekaterina Vylomova, University of Melbourne, Australia

Eleni Metheniti, CLLE-CNRS and IRIT-CNRS

Francis Tyers, Indiana University

Kalika Bali, MSRI Bangalore, India

Koel Dutta Chowdhury, Saarland University (Germany)

Jade Abbott, Retro Rabbit

Jasper Kyle Catapang, University of the Philippines

John P. McCrae, DSI, National University of Ireland Galway

Liangyou Li, Noah’s Ark Lab, Huawei Technologies

Maria Art Antonette Clariño, University of the Philippines Los Baños

Mathias Müller, University of Zurich

Nathaniel Oco, National University (Philippines)

Priya Rani, National University of Ireland Galway

Rico Sennrich, University of Zurich

Sangjee Dondrub, Qinghai Normal University

Santanu Pal, WIPRO AI

Sardana Ivanova, University of Helsinki

Shantipriya Parida, Silo AI

Sina Ahmadi, DSI, National University of Ireland Galway

Sunit Bhattacharya, Charles University

Surafel Melaku Lakew, Amazon AI

Tommi A Pirinen, UiT The Arctic University of Norway, Tromsø

Wen Lai, Center for Information and Language Processing, LMU Munich

Valentin Malykh, Huawei Noah’s Ark lab and Kazan Federal University