Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
Okay, I submitted the EOI, and sent an email with details to the preliminary author list. If anyone is an established Apertium contributor (major contributions, previous publications, or similar) and didn't get the details email but would like to be involved, there's still time—just let me know. -- Jonathan вт, 26 нояб. 2019 г. в 08:26, Jonathan Washington : > > 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 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 >> 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 >>> : >>> > >>> > 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.
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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 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 >> : >> > >> > 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 >> >
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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 > : > > > > 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
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
Hi all, today is the deadline for the Expression of Interest for this. Has this been taken care of? Ilnar Am 21.11.2019 14:46 schrieb Ilnar Salimzianov: Am 21.11.2019 14:45 schrieb Ilnar Salimzianov: Affiliation: independent scholar Email: ilnar nokta at selimcan nokta org ilnar at selimcan nokta org that is 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 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 : 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 *
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
Am 21.11.2019 14:45 schrieb Ilnar Salimzianov: Affiliation: independent scholar Email: ilnar nokta at selimcan nokta org ilnar at selimcan nokta org that is 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 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 : 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
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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 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 : 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
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
I would like to help On Wed, 20 Nov 2019, 22:35 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 > : > > > > 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
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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 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 > > : > >> > >> 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
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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 : 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
Re: [Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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 : > > 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
[Apertium-stuff] Special Issue on Machine Translation for Low-Resource Languages (MT Journal)
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:
