Moses-support Digest, Vol 215, Issue 1

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Today's Topics:

1. CALL FOR PAPERS: Special issue on Machine Translation for
Low-Resource Languages@Language Resources and Evaluation Journal
(Atul Kr. Ojha)


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Message: 1
Date: Wed, 16 Apr 2025 14:30:06 +0100
From: "Atul Kr. Ojha" <shashwatup9k@gmail.com>
To: wmt-tasks@googlegroups.com, mt-list@eamt.org,
siglex-members@googlegroups.com, moses-support
<moses-support@mit.edu>, open-linguistics@googlegroups.com
Subject: [Moses-support] CALL FOR PAPERS: Special issue on Machine
Translation for Low-Resource Languages@Language Resources and
Evaluation Journal
Message-ID:
<CACvVY2gFOEUVtugLShSnXYax8Ko5FDijijdB9WoW64vncbcxFg@mail.gmail.com>
Content-Type: text/plain; charset="utf-8"

Apologies for cross-posting.
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*CALL FOR PAPERS: Language Resources and Evaluation Journal- Special Issue
on Machine Translation for Low-Resource Languages*
https://link.springer.com/collections/gbdgacbgbg

*Guest Editors:*

- Atul Kr. Ojha (Insight Research Ireland Centre for Data Analytics,
DSI, University of Galway, Ireland)
- Chao-Hong Liu (Industrial Technology Research Institute, Potamu
Research Ltd.)
- Ekaterina Vylomova (University of Melbourne, Australia)
- Flammie Pirinen (UiT The Arctic University of Norway, Troms?)
- Jonathan Washington (Swarthmore College, USA)
- Nathaniel Oco (De La Salle University, Philippines)
- Xiaobing Zhao (Minzu University of China)


Machine translation (MT) technologies have been improved significantly in
the last decade using neural MT (NMT) approaches. However, most of these
methods rely on the availability of large parallel data for training the MT
systems, resources which are not available for the majority of language
pairs. Hence, current technologies often fall short in their ability to be
applied to low-resource languages. Developing MT technologies using
relatively small corpora still presents a major challenge for the MT
community. In addition, many methods for developing MT systems still rely
on several natural language processing (NLP) tools to pre-process texts in
source languages and post-process MT outputs in target languages. The
performance of these tools often has a great impact on the quality of the
resulting translation. The availability of MT technologies and NLP tools
can facilitate equal access to information for the speakers of a language
and determine on which side of the digital divide they will end up. The
lack of these technologies for many of the world's languages provides
opportunities both for the field to grow and for making tools available for
speakers of low-resource languages.

In the past few years, several workshops and evaluations have been
organized to promote research on low-resource languages. NIST has been
conducting Low Resource Human Language Technology evaluations (LoReHLT)
annually from 2016 to 2019. In LoReHLT evaluations, there is no training
data in the evaluation language. Participants receive training data in
related languages but need to bootstrap systems in the surprise evaluation
language at the start of the evaluation. Methods for this include pivoting
approaches and taking advantage of linguistic universals. The evaluations
are supported by DARPA's Low Resource Languages for Emergent Incidents
(LORELEI) program, which seeks to advance technologies that are less
dependent on large data resources and that can be quickly pivoted to new
languages within a very short amount of time so that information from any
language can be extracted in a timely manner to provide situation awareness
to emergent incidents. There are also the Workshop on Technologies for MT
of Low-Resource Languages (LoResMT), Special Interest Group on
Under-resourced Languages (SIGUL), Workshop on Resources and Technologies
for Indigenous, Endangered and Lesser-resourced Languages in Eurasia
(EURALI), the Workshop on Deep Learning Approaches for Low-Resource Natural
Language Processing (DeepLo). AfricaNLP, TurkLang, Conference on Machine
Translation (WMT), and International Conference on Spoken Language
Translation (IWSLT) workshop, which provide a venue for sharing research
and working on research and development in this field.

This topical collection solicits original research papers on MT
systems/methods and related NLP tools for low-resource languages in
general. LoReHLT, LORELEI, LoResMT, SIGUL, EURALI, DeepLo, WMT, and IWSLT
participants are very welcome to submit their work to the special issue.
Summary papers on MT research for specific low-resource languages, as well
as extended versions (>40% difference) of published papers from relevant
conferences/workshops, are also welcome.

Topics of the special issue include, but are not limited to:

* Research and review papers on MT systems/methods for low-resource
languages

* Research and review papers on pre-processing and/or post-processing NLP
tools for MT

* Word tokenizers/de-tokenizers for low-resource languages

* Word/morpheme segmenters for low-resource languages

* Use of morphological analyzers and/or morpheme segmenters in MT

* Multilingual/cross-lingual NLP tools for MT

* Review of available corpora of low-resource languages for MT

* 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 and/or NLP tools for low-resource
languages

* Machine translation for language preservation

* Techniques that work across many languages and modalities

* Techniques that are less dependent on large data resources

* Use of language-universal resources

* Bootstrap-trained resources for the short development cycle

* Entity, relation- and event-extraction

* Sentiment detection in MT

* MT Summarisation

* Processing diverse languages, genres (news, social media, etc.) and
modalities (text, speech, video, etc.)

* Speech Translation for low-resource languages

* Multimodal MT for low-resource languages

* MT models using LLMs for low-resource languages

* Generative AI models for low-resource languages

* Evaluation metrics and datasets for low-resource languages


For further information on this initiative, please refer to
https://link.springer.com/collections/gbdgacbgbg


*IMPORTANT DATES*
May 26, 2025: Expression of interest (EOI) via this form:
https://forms.gle/QqeqxZgGfsxP6rZ77
August 26, 2025: Paper submission deadline
December 05, 2025: Revised papers due
March 2026: Publication


* SUBMISSION GUIDELINES*

Authors should follow the "Instructions for Authors
<https://link.springer.com/journal/10579/submission-guidelines> (
https://link.springer.com/journal/10579/submission-guidelines)" on the LRE
journal website <https://link.springer.com/journal/10579>.


Thanks,
Atul
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