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/ > >> > >> 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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