Dear all EAMT members,
I'm Toshiaki Nakazawa from The University of Tokyo, Japan. This is the
call for participation for the MT shared tasks and research papers to
the 8th Workshop on Asian Translation (WAT2021), workshop of
ACL-IJCNLP 2021. Those who are working on machine translation, please
join us.
IMPORTANT DATES
---------------
April 26, 2021 – Translation Task Submission Deadline
April 26, 2021 – Research Paper Submission Deadline
May 28, 2021 – Notification of Acceptance for Research Papers
May 17, 2021 – System Description Paper Submission Deadline
May 28, 2021 – Review Feedback of System Description Papers
June 7, 2021 – Camera-ready Deadline (both Research and System
Description Papers)
August 5-6, 2021 – 2020 Workshop Dates (one of these days)
* All deadlines are calculated at 11:59PM UTC-12
Best regards,
---------------------------------------------------------------------------
WAT2021
(The 8th Workshop on Asian Translation)
in conjunction with ACL-IJCNLP2021
http://lotus.kuee.kyoto-u.ac.jp/WAT/
August 5-6, 2021, Bangkok, Thailand
Following the success of the previous WAT workshops (WAT2014 --
WAT2020), WAT2021 will bring together machine translation researchers
and users to try, evaluate, share and discuss brand-new ideas about
machine translation. For the 8th WAT, we will include the following
new translation tasks:
* MultiIndicMT: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi,
Oriya, Punjabi, Tamil, Telugu <--> English Multilingual Task
* Malayalam Visual Genome: English --> Malayalam Multimodal Task
* Ambiguous MS COCO: English <--> Japanese Multimodal Task
* ArEnMulti30K: English <--> Arabic Multimodal Task
* Restricted Translation Task
together with the following continuing tasks:
* English/Chinese <--> Japanese scientific paper task
* English/Chinese/Korean <--> Japanese patent task
* English <--> Japanese newswire task
* Russian <--> Japanese news commentary task
* Myanmar <--> English mixed-domain task
* Khmer <--> English mixed-domain task
* English <--> Japanese (Flickr30kEnt-JP) multimodal translation task
* English <--> Hindi, Thai, Malay, Indonesian NICT-SAP multilingual
multi-domain task
* English --> Hindi multimodal task
In addition to the shared tasks, the workshop will also feature
scientific papers on topics related to the machine translation,
especially for Asian languages. Topics of interest include, but are
not limited to:
- analysis of the automatic/human evaluation results in the past WAT workshops
- word-/phrase-/syntax-/semantics-/rule-based, neural and hybrid
machine translation
- Asian language processing
- incorporating linguistic information into machine translation
- decoding algorithms
- system combination
- error analysis
- manual and automatic machine translation evaluation
- machine translation applications
- quality estimation
- domain adaptation
- machine translation for low resource languages
- language resources
************************* IMPORTANT NOTICE *************************
Participants of the previous workshop are also required to sign up to WAT2021
********************************************************************
TRANSLATION TASKS
-----------------
The task is to improve the text translation quality for scientific
papers and patent documents. Participants choose any of the subtasks
in which they would like to participate and translate the test data
using their machine translation systems. The WAT organizers will
evaluate the results submitted using automatic evaluation and human
evaluation. We will also provide a baseline machine translation.
Tasks:
* Document-level translation tasks:
- ASPEC+ParaNatCom: English --> Japanese Scientific Paper
- BSD Corpus: English <--> Japanese Business Scene Dialogue
- JIJI Corpus: English <--> Japanese Newswire
- NICT-SAP: Hindi/Thai/Malay/Indonesian <--> English
* Multimodal translation tasks:
- Hindi Visual Genome: English --> Hindi
- Malayalam Visual Genome: English --> Malayalam (NEW!!)
- Flickr30kEnt-JP: English <--> Japanese
- Ambiguous MS COCO: English <--> Japanese (NEW!!)
- ArEnMulti30K: English <--> Arabic (NEW!!)
* Indic tasks:
MultiIndicMT:
Bengali/Gujarati/Hindi/Kannada/Malayalam/Marathi/Odia/Punjabi/Tamil/Telugu
<--> English (NEW and expanded training data and evaluation sets!!)
* ALT+ tasks:
- +UCSY: Myanmar (Burmese) <--> English
- +ECCC: Khmer <--> English
* Patent task:
- JPC2: English/Chinese/Korean <--> Japanese
* News Commentary task:
- JaRuNC: Japanese <--> Russian
* Restricted Translation task
Dataset:
* Scientific paper
WAT uses ASPEC for the dataset including training, development,
development test and test data. Participants of the scientific papers
subtask must get a copy of ASPEC by themselves. ASPEC consists of
approximately 3 million Japanese-English parallel sentences from paper
abstracts (ASPEC-JE) and approximately 0.7 million Japanese-Chinese
paper excerpts (ASPEC-JC)
* Patent
WAT uses JPO Patent Corpus, which is constructed by Japan Patent
Office (JPO). This corpus consists of 1 million English-Japanese
parallel sentences, 1 million Chinese-Japanese parallel sentences, and
1 million Korean-Japanese parallel sentences from patent description
with four categories. Participants of patent tasks are required to get
it on WAT2019 site of JPO Patent Corpus.
- English/Chinese/Korean <--> Japanese:
These tasks evaluate performance of a translation model similarly as
the other translation tasks. Differing from the previous tasks at
WAT2015, WAT2016 and WAT2017, new test sets of these tasks consists
of (a) patent documents published between 2011 and 2013, which were
used in the past years' WAT, and (b) ones published between 2016 and
2017 for each language pair. We will also evaluate performance of the
section (a) so as to compare systems submitted in the past years'
WAT.
- Chinese -> Japanese expression pattern task:
This task evaluates performance of a translation model for each
predifined category of expression patterns, which corresponds to
title of invention (TIT), abstract (ABS), scope of claim (CLM) or
description (DES). Test set of this task consists of sentences each
of which is annotated with a corresponding category of expression
patterns.
* Newswire
WAT uses JIJI Corpus, which is constructed by Jiji Press Ltd. in
collaboration with the National Institute of Information and
Communications Technology (NICT). This corpus consists of a
Japanese-English news corpus of 200K parallel sentences, from Jiji
Press news with various categories. At WAT2021, the organizers newly
added a new document-level translation testset, which consists of
manually filtered test and reference sentences and document-level
context of the test sentences. Participants of the newswire subtask
are required to get it on WAT2021 site of JIJI Corpus.
* News Commentary
WAT uses a manually aligned and cleaned Japanese <--> Russian corpus
from the News Commentary domain to study extremely low resource
situations for distant language pairs. The parallel corpus contains
around 12,000 lines. This year, we invite participants to utilize any
existing monolingual or parallel corpora from WMT 2020 in addition to
those listed on the WAT website. In particular, solutions focusing on
monolingual pretraining and multilingualism are encouraged.
* IT and Wikinews
- Hindi/Thai/Malay/Indonesian <--> English
In collaboration with SAP and NICT, WAT is organising a pilot
translation task to/from English to/from Hindi, Thai, Malay and
Indonesian. The evaluation data belongs to the IT domain (Software
Documentation) and Wikinews domain (Asian Language Treebank).
Participants will be expected to train systems and submit translations
for all language pairs (to and from English) and both domains using
any existing monolingual or parallel data. Given the growing focus on
a universal translation model for multiple languages and domains, WAT
encourages a single multilingual and multi-domain model for all
language pairs and both domains (IT as well as Wikinews). Additional
details will be given on the WAT 2021 website.
* Mixed domain
- Myanmar (Burmese) <--> English
WAT uses UCSY Corpus and ALT Corpus. The UCSY corpus and a portion of
the ALT corpus are use as training data, which are around 220,000
lines of sentences and phrases. The development and test data are
from the ALT corpus.
- Khmer <--> English
WAT uses ECCC Corpus and ALT Corpus. The ECCC corpus and a portion of
the ALT corpus are used as training data, which are around 120,000
lines of sentences and phrases. The development and test data are
from the ALT corpus.
* Indic
- Indian language <--> English multilingual translation task. This
task is a successor to the 2018 and the 2020 tasks with major
improvements. . There has been an increase in the available datasets
for Indian languages in the past few years along with major advances
in multilingual learning. The task will involve training a
multilingual model for 10 Indian languages to English (and
vice-versa) translation. The goal is to encourage exploration of
methods which utilize multilingualism and language relatedness to
improve translation quality for low-resource languages while having a
single, compact translation model. The evaluation set is 11-way
parallel enabling the potential evaluation of non-English centric
language pairs.
* Multimodal
Given the growing interest in multimodal NLP and the warm response
from the participants for the “WAT 2019 and 2020 Multimodal
Translation Tasks”, WAT will evaluate the following multimodal tasks:
- English --> Hindi Multimodal (Visual Genome) WAT will continue
organizing the multimodal English --> Hindi translation task where
the input will be text and an Image and the output will be a caption
(text). The training set contains around 30,000 segments. Additional
details will be given on the task website.
- English --> Malayalam Multimodal (Visual Genome) WAT will
organize a new multimodal English --> Malayalam translation task
where the input will be text and an Image and the output will be a
caption (text). The training set contains around 30,000 segments.
Additional details will be given on the task website.
- Japanese <--> English Multimodal (Flickr30kEnt-JP)
WAT will continue the Flickr30kEnt-JP task using the corpus with the
same name for this task. https://github.com/nlab-mpg/Flickr30kEnt-JP
- Arabic <--> English Multimodal (ArEnMulti30K) WAT will organize a
new multimodal Arabic <--> English translation task where the input
will be text and an Image and the output will be a caption (text). The
training set contains around 30,000 segments. Additional details will
be given on the task website.
- Japanese <--> English Multimodal (Ambiguous MS COCO) WAT will
organize an additional multimodal Japanese <--> English translation
task where the evaluation set, Ambiguous MS COCO, will focus on
translation of ambiguous words and sentences. Along with the
Flickr30kEnt-JP dataset, the MS COCO English data may also be used.
Additional details will be given on the task website.
EVALUATION
----------
Automatic evaluation:
We are providing an automatic evaluation server. It is free for
everyone, but you need to create an account for evaluation. Just
showing the list of evaluation results does not require an account.
Sign-up: http://lotus.kuee.kyoto-u.ac.jp/WAT/WAT2021/
Eval. result: http://lotus.kuee.kyoto-u.ac.jp/WAT/evaluation/
Human evaluation:
Both crowdsourcing evaluation and JPO adequacy evaluation will be
carried out for selected subtasks and selected submitted systems (the
details will be announced later).
ORGANIZERS
----------
- Toshiaki Nakazawa, The University of Tokyo, Japan [GENERAL,
ASPEC+ParaNatCom, BSD]
- Hideki Nakayama, The University of Tokyo, Japan [Flickr30kEnt-JP]
- Isao Goto, Japan Broadcasting Corporation (NHK), Japan [GENERAL, JIJI]
- Hidaya Mino, Japan Broadcasting Corporation (NHK), Japan [GENERAL, JIJI]
- Chenchen Ding, National Institute of Information and Communications
Technology (NICT), Japan [GENERAL, ALT+UCSY, ALT+ECCC]
- Raj Dabre, National Institute of Information and Communications
Technology (NICT), Japan [MultiIndicMT, ALT+SAP, Global voices]
- Anoop Kunchookuttan, Microsoft AI and Research, India [MultiIndicMT]
- Shohei Higashiyama, National Institute of Information and
Communications Technology (NICT), Japan [JPC]
- Hiroshi Manabe, National Institute of Information and Communications
Technology (NICT), Japan [GENERAL]
- Win Pa Pa, University of Computer Studies, Yangon (UCSY), Myanmar [ALT+UCSY]
- Shantipriya Parida, Idiap Research Institute, Martigny, Switzerland
[Hindi Visual Genome, Malayalam Visual Genome]
- Ondřej Bojar, Charles University, Prague, Czech Republic [Hindi
Visual Genome, Malayalam Visual Genome]
- Chenhui Chu, Kyoto University, Japan [Ambiguous MS COCO]
- Mahmoud Al-Ayyoub, Jordan University of Science and Technology,
Jordan [Multi30K]
- Ali Fadel, Jordan University of Science and Technology, Jordan [Multi30K]
- Roweida Mohammed, Jordan University of Science and Technology,
Jordan [Multi30K]
- Inad Aljarrah, Jordan University of Science and Technology, Jordan [Multi30K]
- Akiko Eriguchi, Microsoft, USA [Restricted Translation]
- Yusuke Oda, LegalForce, Japan [Restricted Translation]
- Katsuhito Sudoh, Nara Institute of Science and Technology (NAIST),
Japan [GENERAL]
- Sadao Kurohashi, Kyoto University, Japan [GENERAL]
- Pushpak Bhattacharyya, Indian Institute of Technology Patna (IITP),
India [GENERAL]
CONTACT
-------
wat-organi...@googlegroups.com
I'm Toshiaki Nakazawa from The University of Tokyo, Japan. This is the
call for participation for the MT shared tasks and research papers to
the 8th Workshop on Asian Translation (WAT2021), workshop of
ACL-IJCNLP 2021. Those who are working on machine translation, please
join us.
IMPORTANT DATES
---------------
April 26, 2021 – Translation Task Submission Deadline
April 26, 2021 – Research Paper Submission Deadline
May 28, 2021 – Notification of Acceptance for Research Papers
May 17, 2021 – System Description Paper Submission Deadline
May 28, 2021 – Review Feedback of System Description Papers
June 7, 2021 – Camera-ready Deadline (both Research and System
Description Papers)
August 5-6, 2021 – 2020 Workshop Dates (one of these days)
* All deadlines are calculated at 11:59PM UTC-12
Best regards,
---------------------------------------------------------------------------
WAT2021
(The 8th Workshop on Asian Translation)
in conjunction with ACL-IJCNLP2021
http://lotus.kuee.kyoto-u.ac.jp/WAT/
August 5-6, 2021, Bangkok, Thailand
Following the success of the previous WAT workshops (WAT2014 --
WAT2020), WAT2021 will bring together machine translation researchers
and users to try, evaluate, share and discuss brand-new ideas about
machine translation. For the 8th WAT, we will include the following
new translation tasks:
* MultiIndicMT: Bengali, Gujarati, Hindi, Kannada, Malayalam, Marathi,
Oriya, Punjabi, Tamil, Telugu <--> English Multilingual Task
* Malayalam Visual Genome: English --> Malayalam Multimodal Task
* Ambiguous MS COCO: English <--> Japanese Multimodal Task
* ArEnMulti30K: English <--> Arabic Multimodal Task
* Restricted Translation Task
together with the following continuing tasks:
* English/Chinese <--> Japanese scientific paper task
* English/Chinese/Korean <--> Japanese patent task
* English <--> Japanese newswire task
* Russian <--> Japanese news commentary task
* Myanmar <--> English mixed-domain task
* Khmer <--> English mixed-domain task
* English <--> Japanese (Flickr30kEnt-JP) multimodal translation task
* English <--> Hindi, Thai, Malay, Indonesian NICT-SAP multilingual
multi-domain task
* English --> Hindi multimodal task
In addition to the shared tasks, the workshop will also feature
scientific papers on topics related to the machine translation,
especially for Asian languages. Topics of interest include, but are
not limited to:
- analysis of the automatic/human evaluation results in the past WAT workshops
- word-/phrase-/syntax-/semantics-/rule-based, neural and hybrid
machine translation
- Asian language processing
- incorporating linguistic information into machine translation
- decoding algorithms
- system combination
- error analysis
- manual and automatic machine translation evaluation
- machine translation applications
- quality estimation
- domain adaptation
- machine translation for low resource languages
- language resources
************************* IMPORTANT NOTICE *************************
Participants of the previous workshop are also required to sign up to WAT2021
********************************************************************
TRANSLATION TASKS
-----------------
The task is to improve the text translation quality for scientific
papers and patent documents. Participants choose any of the subtasks
in which they would like to participate and translate the test data
using their machine translation systems. The WAT organizers will
evaluate the results submitted using automatic evaluation and human
evaluation. We will also provide a baseline machine translation.
Tasks:
* Document-level translation tasks:
- ASPEC+ParaNatCom: English --> Japanese Scientific Paper
- BSD Corpus: English <--> Japanese Business Scene Dialogue
- JIJI Corpus: English <--> Japanese Newswire
- NICT-SAP: Hindi/Thai/Malay/Indonesian <--> English
* Multimodal translation tasks:
- Hindi Visual Genome: English --> Hindi
- Malayalam Visual Genome: English --> Malayalam (NEW!!)
- Flickr30kEnt-JP: English <--> Japanese
- Ambiguous MS COCO: English <--> Japanese (NEW!!)
- ArEnMulti30K: English <--> Arabic (NEW!!)
* Indic tasks:
MultiIndicMT:
Bengali/Gujarati/Hindi/Kannada/Malayalam/Marathi/Odia/Punjabi/Tamil/Telugu
<--> English (NEW and expanded training data and evaluation sets!!)
* ALT+ tasks:
- +UCSY: Myanmar (Burmese) <--> English
- +ECCC: Khmer <--> English
* Patent task:
- JPC2: English/Chinese/Korean <--> Japanese
* News Commentary task:
- JaRuNC: Japanese <--> Russian
* Restricted Translation task
Dataset:
* Scientific paper
WAT uses ASPEC for the dataset including training, development,
development test and test data. Participants of the scientific papers
subtask must get a copy of ASPEC by themselves. ASPEC consists of
approximately 3 million Japanese-English parallel sentences from paper
abstracts (ASPEC-JE) and approximately 0.7 million Japanese-Chinese
paper excerpts (ASPEC-JC)
* Patent
WAT uses JPO Patent Corpus, which is constructed by Japan Patent
Office (JPO). This corpus consists of 1 million English-Japanese
parallel sentences, 1 million Chinese-Japanese parallel sentences, and
1 million Korean-Japanese parallel sentences from patent description
with four categories. Participants of patent tasks are required to get
it on WAT2019 site of JPO Patent Corpus.
- English/Chinese/Korean <--> Japanese:
These tasks evaluate performance of a translation model similarly as
the other translation tasks. Differing from the previous tasks at
WAT2015, WAT2016 and WAT2017, new test sets of these tasks consists
of (a) patent documents published between 2011 and 2013, which were
used in the past years' WAT, and (b) ones published between 2016 and
2017 for each language pair. We will also evaluate performance of the
section (a) so as to compare systems submitted in the past years'
WAT.
- Chinese -> Japanese expression pattern task:
This task evaluates performance of a translation model for each
predifined category of expression patterns, which corresponds to
title of invention (TIT), abstract (ABS), scope of claim (CLM) or
description (DES). Test set of this task consists of sentences each
of which is annotated with a corresponding category of expression
patterns.
* Newswire
WAT uses JIJI Corpus, which is constructed by Jiji Press Ltd. in
collaboration with the National Institute of Information and
Communications Technology (NICT). This corpus consists of a
Japanese-English news corpus of 200K parallel sentences, from Jiji
Press news with various categories. At WAT2021, the organizers newly
added a new document-level translation testset, which consists of
manually filtered test and reference sentences and document-level
context of the test sentences. Participants of the newswire subtask
are required to get it on WAT2021 site of JIJI Corpus.
* News Commentary
WAT uses a manually aligned and cleaned Japanese <--> Russian corpus
from the News Commentary domain to study extremely low resource
situations for distant language pairs. The parallel corpus contains
around 12,000 lines. This year, we invite participants to utilize any
existing monolingual or parallel corpora from WMT 2020 in addition to
those listed on the WAT website. In particular, solutions focusing on
monolingual pretraining and multilingualism are encouraged.
* IT and Wikinews
- Hindi/Thai/Malay/Indonesian <--> English
In collaboration with SAP and NICT, WAT is organising a pilot
translation task to/from English to/from Hindi, Thai, Malay and
Indonesian. The evaluation data belongs to the IT domain (Software
Documentation) and Wikinews domain (Asian Language Treebank).
Participants will be expected to train systems and submit translations
for all language pairs (to and from English) and both domains using
any existing monolingual or parallel data. Given the growing focus on
a universal translation model for multiple languages and domains, WAT
encourages a single multilingual and multi-domain model for all
language pairs and both domains (IT as well as Wikinews). Additional
details will be given on the WAT 2021 website.
* Mixed domain
- Myanmar (Burmese) <--> English
WAT uses UCSY Corpus and ALT Corpus. The UCSY corpus and a portion of
the ALT corpus are use as training data, which are around 220,000
lines of sentences and phrases. The development and test data are
from the ALT corpus.
- Khmer <--> English
WAT uses ECCC Corpus and ALT Corpus. The ECCC corpus and a portion of
the ALT corpus are used as training data, which are around 120,000
lines of sentences and phrases. The development and test data are
from the ALT corpus.
* Indic
- Indian language <--> English multilingual translation task. This
task is a successor to the 2018 and the 2020 tasks with major
improvements. . There has been an increase in the available datasets
for Indian languages in the past few years along with major advances
in multilingual learning. The task will involve training a
multilingual model for 10 Indian languages to English (and
vice-versa) translation. The goal is to encourage exploration of
methods which utilize multilingualism and language relatedness to
improve translation quality for low-resource languages while having a
single, compact translation model. The evaluation set is 11-way
parallel enabling the potential evaluation of non-English centric
language pairs.
* Multimodal
Given the growing interest in multimodal NLP and the warm response
from the participants for the “WAT 2019 and 2020 Multimodal
Translation Tasks”, WAT will evaluate the following multimodal tasks:
- English --> Hindi Multimodal (Visual Genome) WAT will continue
organizing the multimodal English --> Hindi translation task where
the input will be text and an Image and the output will be a caption
(text). The training set contains around 30,000 segments. Additional
details will be given on the task website.
- English --> Malayalam Multimodal (Visual Genome) WAT will
organize a new multimodal English --> Malayalam translation task
where the input will be text and an Image and the output will be a
caption (text). The training set contains around 30,000 segments.
Additional details will be given on the task website.
- Japanese <--> English Multimodal (Flickr30kEnt-JP)
WAT will continue the Flickr30kEnt-JP task using the corpus with the
same name for this task. https://github.com/nlab-mpg/Flickr30kEnt-JP
- Arabic <--> English Multimodal (ArEnMulti30K) WAT will organize a
new multimodal Arabic <--> English translation task where the input
will be text and an Image and the output will be a caption (text). The
training set contains around 30,000 segments. Additional details will
be given on the task website.
- Japanese <--> English Multimodal (Ambiguous MS COCO) WAT will
organize an additional multimodal Japanese <--> English translation
task where the evaluation set, Ambiguous MS COCO, will focus on
translation of ambiguous words and sentences. Along with the
Flickr30kEnt-JP dataset, the MS COCO English data may also be used.
Additional details will be given on the task website.
EVALUATION
----------
Automatic evaluation:
We are providing an automatic evaluation server. It is free for
everyone, but you need to create an account for evaluation. Just
showing the list of evaluation results does not require an account.
Sign-up: http://lotus.kuee.kyoto-u.ac.jp/WAT/WAT2021/
Eval. result: http://lotus.kuee.kyoto-u.ac.jp/WAT/evaluation/
Human evaluation:
Both crowdsourcing evaluation and JPO adequacy evaluation will be
carried out for selected subtasks and selected submitted systems (the
details will be announced later).
ORGANIZERS
----------
- Toshiaki Nakazawa, The University of Tokyo, Japan [GENERAL,
ASPEC+ParaNatCom, BSD]
- Hideki Nakayama, The University of Tokyo, Japan [Flickr30kEnt-JP]
- Isao Goto, Japan Broadcasting Corporation (NHK), Japan [GENERAL, JIJI]
- Hidaya Mino, Japan Broadcasting Corporation (NHK), Japan [GENERAL, JIJI]
- Chenchen Ding, National Institute of Information and Communications
Technology (NICT), Japan [GENERAL, ALT+UCSY, ALT+ECCC]
- Raj Dabre, National Institute of Information and Communications
Technology (NICT), Japan [MultiIndicMT, ALT+SAP, Global voices]
- Anoop Kunchookuttan, Microsoft AI and Research, India [MultiIndicMT]
- Shohei Higashiyama, National Institute of Information and
Communications Technology (NICT), Japan [JPC]
- Hiroshi Manabe, National Institute of Information and Communications
Technology (NICT), Japan [GENERAL]
- Win Pa Pa, University of Computer Studies, Yangon (UCSY), Myanmar [ALT+UCSY]
- Shantipriya Parida, Idiap Research Institute, Martigny, Switzerland
[Hindi Visual Genome, Malayalam Visual Genome]
- Ondřej Bojar, Charles University, Prague, Czech Republic [Hindi
Visual Genome, Malayalam Visual Genome]
- Chenhui Chu, Kyoto University, Japan [Ambiguous MS COCO]
- Mahmoud Al-Ayyoub, Jordan University of Science and Technology,
Jordan [Multi30K]
- Ali Fadel, Jordan University of Science and Technology, Jordan [Multi30K]
- Roweida Mohammed, Jordan University of Science and Technology,
Jordan [Multi30K]
- Inad Aljarrah, Jordan University of Science and Technology, Jordan [Multi30K]
- Akiko Eriguchi, Microsoft, USA [Restricted Translation]
- Yusuke Oda, LegalForce, Japan [Restricted Translation]
- Katsuhito Sudoh, Nara Institute of Science and Technology (NAIST),
Japan [GENERAL]
- Sadao Kurohashi, Kyoto University, Japan [GENERAL]
- Pushpak Bhattacharyya, Indian Institute of Technology Patna (IITP),
India [GENERAL]
CONTACT
-------
wat-organi...@googlegroups.com
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