Affiliation: independent scholar
Email: ilnar nokta at selimcan nokta org
Ilnar
Am 21.11.2019 14:19 schrieb Sevilay Bayatlı:
Ilnar,
I have added you, we need your affiliation and email address too.
Sevilay
On Thu, Nov 21, 2019 at 7:32 AM Ilnar Salimzianov <il...@selimcan.org>
wrote:
Hey,
I don't remember whether I have said so already, but I'm in :)
Best,
Ilnar
Am 20.11.2019 18:04 schrieb Jonathan Washington:
Hi all,
This is just a reminder that the expression of interest for this
volume is due in less than a week!
The expression of interest is easy: just a title, list of
authors, and
a short description.
If anyone else would like to help out with the updated Apertium
paper
that we're planning to submit, then please get in touch.
--
Jonathan
пт, 1 нояб. 2019 г. в 22:21, Jonathan Washington
<jonathan.n.washing...@gmail.com>:
Hi all,
Below please find a revised CFP for the Machine Translation
Special
Issue on MT for Low-Resource Languages.
=====
CALL FOR PAPERS: Machine Translation Journal
Special Issue on Machine Translation for Low-Resource Languages
https://www.springer.com/computer/ai/journal/10590/ [1]
GUEST EDITORS (Listed alphabetically)
• Alina Karakanta (FBK-Fondazione Bruno Kessler)
• Audrey N. Tong (NIST)
• Chao-Hong Liu (ADAPT Centre/Dublin City University)
• Ian Soboroff (NIST)
• Jonathan Washington (Swarthmore College)
• Oleg Aulov (NIST)
• Xiaobing Zhao (Minzu University of China)
Machine translation (MT) technologies have been improved
significantly
in the last two decades, with developments in phrase-based
statistical
MT (SMT) and recently neural MT (NMT). 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, and 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 recent 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) and the Workshop on Deep
Learning
Approaches for Low-Resource Natural Language Processing
(DeepLo),
which provide a venue for sharing research and working on the
research
and development in this field.
This special issue solicits original research papers on MT
systems/methods and related NLP tools for low-resource languages
in
general. LoReHLT, LORELEI, LoResMT and DeepLo 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 of MT systems/methods for
low-resource
languages
* Research and review papers of 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 short development cycle
* Entity-, relation- and event-extraction
* Sentiment detection
* Summarization
* Processing diverse languages, genres (news, social media,
etc.) and
modalities (text, speech, video, etc.)
IMPORTANT DATES
November 26, 2019: Expression of interest (EOI)
February 25, 2020: Paper submission deadline
July 7, 2020: Camera-ready papers due
December, 2020: Publication
SUBMISSION GUIDELINES
o For EOI, please submit via the link:
https://forms.gle/mAQH4qaPTuzDhEceA [2]
o For paper submission, please go to the MT journal website
https://link.springer.com/journal/10590 [3] and select this
special issue
o Authors should follow the "Instructions for Authors"
o Recommended length of paper is 15 pages
=====
--
Jonathan
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Links:
------
[1] https://www.springer.com/computer/ai/journal/10590/
[2] https://forms.gle/mAQH4qaPTuzDhEceA
[3] https://link.springer.com/journal/10590
[4] https://lists.sourceforge.net/lists/listinfo/apertium-stuff
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