LoResMT: Submission Deadline Extended to July 1st

[John Ortega]

=== Submission Deadline Extended to July 1st ===

The Fourth Workshop on Technologies for MT of Low-Resource Languages
(LoResMT 2021)
https://sites.google.com/view/loresmt/
@ MT Summit XVIII – 2021
The 18th biennial conference of the International Association of Machine
Translation
16-20 August 2021, Orlando, Florida, USA
Invited Speakers
(Listed alphabetically)
Barry Haddow
University of Edinburgh
Catherine Muthoni Gitau
African Institute for Mathematical Sciences (AIMS)
Mathias Müller
Institut für Computerlinguistik, Universität Zürich
Mona Diab
Facebook, George Washington University

SCOPE
Based on the success of past low-resource machine translation (MT)
workshops at AACL-IJCNLP 2020 (http://aacl2020.org/), MT Summit 2019
(https://www.mtsummit2019.com) and AMTA 2018 (https://amtaweb.org/), we
introduce the fourth LoResMT workshop at MT Summit 2021. Like its
predecessors, this workshop will bring together researchers and
translators of low-resource languages to compare and contrast how each
use digital technology for translation. Specifically, the workshop
focuses on novel advances on the coverage of even more languages than
past workshops with different geographical presence, degree of diffusion
and digitalization.

We solicit original work on low-resource translation which includes, but
is not limited to, MT systems that include word
tokenizers/de-tokenizers, word segmenters, morphological analyzers, and
more. We furthermore invite work that includes MT systems based on
neural networks along with their methods, natural language processing
approaches, and overall coverage of low-resource languages.
Additionally, novel work covering translations of COVID-related text and
their practical use for low-resource communities are of high interest.

The goal of this workshop is to begin to close the gap between
low-resource translation systems and their practical use in the real
world. Online systems and original research that can be used by native
speakers of low-resource languages is of particular interest. Therefore,
It will be beneficial if the evaluations of these tools in research
papers include their impact on the quality of MT output and how they can
be used in the real world.

SHARED TASKS
We are happy to announce the introduction of new shared tasks focused on
the building of MT systems for COVID-related texts. The task aims to
encourage research on MT systems involving three low-resource language
pairs:

(1) Taiwanese Sign Language <> Traditional Chinese (> 100,000 pairs)
(2) English <> Irish
(3) English <> Marathi

The training, development, and test sets for the three groups will be
released shortly (please see the important dates). Updated information
will be available on the LoResMT website
(https://sites.google.com/view/loresmt/) and in the Google Group
(https://groups.google.com/g/loresmt2021/).

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
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 MT Summit 2021 style templates (PDF, LaTeX, Word). Accepted
papers will be published on-line in the MT Summit 2021 proceedings and
will be presented at the conference either orally or as a poster.

Submissions must be anonymized and should be done using the official
conference management system
(https://cmt3.research.microsoft.com/MTSUMMIT2021). 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 required for accepted papers by the main conference web
page (https://amtaweb.org/mt-summit2021/).

IMPORTANT DATES
March 25, 2021 – Call for papers released
May 04, 2021 – Second call for papers
May 20, 2021 – Third call for papers
July 01 , 2021 – Paper submissions due
July 15 , 2021 – Notification of acceptance
July 22, 2021 –  Camera-ready due
August 5, 2021 –  Video recordings due
August 16, 2021 - LoResMT workshop

CONTACT
LoResMT 2021 Workshop Chair:
John Ortega (jor...@cs.nyu.edu)

Shared Task Chairs:
Atul Kr. Ojha (atulkum...@insight-centre.org), for inquiries on
Marathi and Irish MT tasks
Chao-Hong Liu (ch....@acm.org), for inquiries on Sign Language MT task
Katharina Kann (kathari...@colorado.edu), for general inquiries

ORGANIZING COMMITTEE (listed alphabetically)
Atul Kr. Ojha    DSI, National University of Ireland Galway & Panlingua
Language Processing LLP
Chao-Hong Liu    Potamu Research Ltd
Jade Abbott    Retro Rabbit
John Ortega    New York University
Jonathan Washington    Swarthmore College
Katharina Kann University of Colorado at Boulder
Nathaniel Oco    National University (Philippines)
Surafel Melaku Lakew    Amazon AI
Tommi A Pirinen    University of Hamburg
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, ADAPT, Dublin City University
Alina Karakanta, Fondazione Bruno Kessler
Amirhossein Tebbifakhr, Fondazione Bruno Kessler
Anna Currey, Amazon Web Services
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
Beatrice Savold, University of Trento
Bogdan Babych, Heidelberg University
Chao-Hong Liu, Potamu Research Ltd
Duygu Ataman, University of Zurich
Eleni Metheniti, CLLE-CNRS and IRIT-CNRS
Francis Tyers, Indiana University
Kalika Bali, MSRI Bangalore, India
Katharina Kann University of Colorado at Boulder
Koel Dutta Chowdhury, Saarland University (Germany)
Jasper Kyle Catapang, University of the Philippines
John P. McCrae, DSI, National University of Ireland Galway
John Ortega, New York University
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
Shabnam Tafreshi, University of Maryland
Shantipriya Parida, Idiap Research Institute
Sina Ahmadi, DSI, National University of Ireland Galway
Sunit Bhattacharya, Charles University
Surafel Melaku Lakew, Amazon AI
Tommi A Pirinen, University of Hamburg
Valentin Malykh, Huawei Noah’s Ark lab and Kazan Federal University