I can take care of it in an hour or two. Thanks for the reminder, Ilnar, and for the organisational help, Sevilay!
-- Jonathan On Tue, Nov 26, 2019, 06:59 Sevilay Bayatlı <sevilaybaya...@gmail.com> wrote: > Hi, > I think we do, we already have the list of authors and draft of the > paper. We need someone to submit a title, list of authors, and > a short description. > > Sevilay > > > On Wed, Nov 20, 2019 at 8:05 PM Jonathan Washington < > jonathan.n.washing...@gmail.com> wrote: > >> 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/ >> > >> > 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 >> > o For paper submission, please go to the MT journal website >> > https://link.springer.com/journal/10590 and select this special issue >> > o Authors should follow the "Instructions for Authors" >> > o Recommended length of paper is 15 pages >> > ===== >> > >> > -- >> > Jonathan >> >> >> _______________________________________________ >> Apertium-stuff mailing list >> Apertium-stuff@lists.sourceforge.net >> https://lists.sourceforge.net/lists/listinfo/apertium-stuff >> > _______________________________________________ > Apertium-stuff mailing list > Apertium-stuff@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/apertium-stuff >
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