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
