Continuous integration is a DevOps software development practice where developers regularly merge their code changes into a central repository, after which automated builds and tests are run. Continuous integration most often refers to the build or integration stage of the software release process and entails both an automation component (e.g. a CI or build service) and a cultural component (e.g. learning to integrate frequently). The key goals of continuous integration are to find and address bugs quicker, improve software quality, and reduce the time it takes to validate and release new software updates.
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In the past, developers on a team might work in isolation for an extended period of time and only merge their changes to the master branch once their work was completed. This made merging code changes difficult and time-consuming, and also resulted in bugs accumulating for a long time without correction. These factors made it harder to deliver updates to customers quickly.
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- The AW Server delivers 3D visualization throughout your enterprise including any remote reading location that is on your enterprise network. GE's thin client technology converts virtually any PC 1, MAC 1 or RIS/PACS 2 into a high-end 3D post-processing station without the need for expensive dedicated graphics processors.
With continuous integration, developers frequently commit to a shared repository using a version control system such as Git. Prior to each commit, developers may choose to run local unit tests on their code as an extra verification layer before integrating. A continuous integration service automatically builds and runs unit tests on the new code changes to immediately surface any errors.
WMT provides high value, custom, secure business process automation solutions for internal clients. We are currently transitioning our applications from the legacy Domino platform to Appian which. Security Testing as a Service: TaaS scans the applications and websites for any vulnerability Key TaaS Features. Software Testing as a Service over Cloud. Once user scenarios are created, and the test is designed, these service providers deliver servers to generate virtual traffic across the globe.
Continuous integration refers to the build and unit testing stages of the software release process. Every revision that is committed triggers an automated build and test.
With continuous delivery, code changes are automatically built, tested, and prepared for a release to production. Continuous delivery expands upon continuous integration by deploying all code changes to a testing environment and/or a production environment after the build stage.
Continuous integration helps your team be more productive by freeing developers from manual tasks and encouraging behaviors that help reduce the number of errors and bugs released to customers.
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With more frequent testing, your team can discover and address bugs earlier before they grow into larger problems later.
Continuous integration helps your team deliver updates to their customers faster and more frequently.
You can practice continuous integration on AWS in several ways.
Set up a continuous integration workflow with AWS CodePipeline, which lets you build a workflow that builds code in AWS CodeBuild every time you commit a change.
Home
This conference builds on a series of annual workshops and conferences on statistical machine translation, going back to 2006:
- the NAACL-2006 Workshop on Statistical Machine Translation,
- the ACL-2007 Workshop on Statistical Machine Translation,
- the ACL-2008 Workshop on Statistical Machine Translation,
- the EACL-2009 Workshop on Statistical Machine Translation,
- the ACL-2010 Workshop on Statistical Machine Translation
- the EMNLP-2011 Workshop on Statistical Machine Translation,
- the NAACL-2012 Workshop on Statistical Machine Translation,
- the ACL-2013 Workshop on Statistical Machine Translation,
- the ACL-2014 Workshop on Statistical Machine Translation,
- the EMNLP-2015 Workshop on Statistical Machine Translation,
- the First Conference on Machine Translation (at ACL-2016).
IMPORTANT DATES
| Release of training data for shared tasks | January/February, 2017 |
| Evaluation periods for shared tasks | April/May, 2017 |
| Paper submission deadline | June 9th, 2017 (Midnight, UTC -11) |
| Paper notification | June 30th, 2017 |
| Camera-ready version due | July 14th, 2017 |
| Conference in Copenhagen | September 7-8, 2017 |
OVERVIEW
This year's conference will feature the following shared tasks:
- a news translation task,
- a biomedical translation task ,
- an automatic post-editing task,
- a metrics task (assess MT quality given reference translation).
- a quality estimation task (assess MT quality without access to any reference),
- a multimodal translation task
- a task dedicated to the training of neural MT systems
- a task on bandit learning for MT
In addition to the shared tasks, the conference will also feature scientific papers on topics related to MT. Topics of interest include, but are not limited to:
- word-based, phrase-based, syntax-based, semantics-based SMT
- neural machine translation
- using comparable corpora for SMT
- incorporating linguistic information into SMT
- decoding
- system combination
- error analysis
- manual and automatic method for evaluating MT
- scaling MT to very large data sets
REGISTRATION
Registration will be handled by EMNLP 2017.NEWS TRANSLATION TASK
The first shared task which will examine translation between the following language pairs:
- English-Chinese and Chinese-English NEW
- English-Czech and Czech-English
- English-Finnish and Finnish-English
- English-German and German-English
- English-Latvian and Latvian-English NEW
- English-Russian and Russian-English
- English-Turkish and Turkish-English
All participants who submit entries will have their translations evaluated. We will evaluate translation performance by human judgment. To facilitate the human evaluation we will require participants in the shared tasks to manually judge some of the submitted translations. For each team, this will amount to ranking 300 sets of 5 translations, per language pair submitted.
BIOMEDICAL TRANSLATION TASK
In this second edition of this task, we will evaluate systems for the translation of biomedical documents for the following languages pairs:
- English-Czech and Czech-English NEW
- English-French and French-English
- English-German and German-English NEW
- English-Hungarian and Hungarian-English NEW
- English-Polish and Polish-English NEW
- English-Portuguese and Portuguese-English
- English-Romanian and Romanian-English NEW
- English-Spanish and Spanish-English
- English-Swedish and Swedish-English NEW
Parallel corpora will be available for all language pairs but also monoligual corpora for some languages. Evaluation will be carried out both automatically and manually.
AUTOMATIC POST-EDITING TASK
This shared task will examine automatic methods for correcting errors produced by machine translation (MT) systems. Automatic Post-editing (APE) aims at improving MT output in black box scenarios, in which the MT system is used 'as is' and cannot be modified.From the application point of view APE components would make it possible to:
- Cope with systematic errors of an MT system whose decoding process is not accessible
- Provide professional translators with improved MT output quality to reduce (human) post-editing effort
In this third edition of the task, the evaluation will focus on English-German (IT domain) and German-English (Medical domain)
METRICS TASK
The metrics task (also called evaluation task) will assess automatic evaluation metrics' ability to:
- Evaluate systems on their overall performance on the test set
- Evaluate systems on a sentence by sentence level
Participants in the shared evaluation task will use their automatic evaluation metrics to score the output from the translation task and the NMT training task. Dead zedclout games. In addition to MT outputs from the other two tasks, the participants will be provided with reference translations. We will measure the correlation of automatic evaluation metrics with the human judgments.
NEURAL MT TRAINING TASK (NMT TRAINING TASK)
This task will assess your team's ability to train a fixed neural MT model given fixed data.Participants in the NMT training task will be given complete a Neural Monkey configuration file which describes the neural model. Training and validation data with fixed pre-processing scheme will be also given (English-to-Czech and Czech-to-English translation).
The participants will be expected to submit the variables file, i.e. the trained neural network, for one or both of the translation directions. We will use the variables and a fixed revision of Neural Monkey to translate official WMT17 test set. The outputs of the various configurations of the system will be scored using the standard manual evaluation procedure.
BANDIT LEARNING TASK
Bandit Learning for MT is a framework to train and improve MT systems by learning from weak or partial feedback: Instead of a gold-standard human-generated translation, the learner only receives feedback to a single proposed translation (this is why it is called partial), in form of a translation quality judgement (which can be as weak as a binary acceptance/rejection decision).
In this task, the user feedback will be simulated by a service hosted on Amazon Web Services (AWS), where participants can submit translations and receive feedback and use this feedback for training an MT model (German-to-English, e-commerce). Reference translations will not be revealed at any point, also evaluations are done via the service. The goal of this task is to find systems that learn efficiently and effectively from this type of feedback, i.e. they learn fast and achieve high translation quality without references.
PAPER SUBMISSION INFORMATION
Submissions will consist of regular full papers of 6-10 pages, plus additional pages for references, formatted following the EMNLP 2017 guidelines. In addition, shared task participants will be invited to submit short papers (suggested length: 4-6 pages, plus references) describing their systems or their evaluation metrics. Both submission and review processes will be handled electronically. Note that regular papers must be anonymized, while system descriptions do not need to be.
Research papers that have been or will be submitted to other meetings or publications must indicate this at submission time, and must be withdrawn from the other venues if accepted and published at WMT 2017. We will not accept for publication papers that overlap significantly in content or results with papers that have been or will be published elsewhere. It is acceptable to submit work that has been made available as a technical report (or similar, e.g. in arXiv) without citing it. This double submission policy only applies to research papers, so system papers can have significant overlap with other published work, if it is relevant to the system description.
We encourage individuals who are submitting research papers to evaluate their approaches using the training resources provided by this conference and past workshops, so that their experiments can be repeated by others using these publicly available corpora.
POSTER FORMAT
A0 Landscape.ANNOUNCEMENTS
| Subscribe to to the announcement list for WMT by entering your e-mail address below. This list will be used to announce when the test sets are released, to indicate any corrections to the training sets, and to amend the deadlines as needed. |
| You can read past announcements on the Google Groups page for WMT. These alsoinclude an archive of announcements from earlier workshops. |
INVITED TALK
Holger Schwenk (Facebook)Multilingual Representions and Applications in NLP
ORGANIZERS
Ondřej Bojar (Charles University in Prague)Christian Buck (University of Edinburgh)
Rajen Chatterjee (FBK)
Christian Federmann (MSR)
Yvette Graham (DCU)
Barry Haddow (University of Edinburgh)
Matthias Huck (University of Edinburgh)
Antonio Jimeno Yepes (IBM Research Australia)
Philipp Koehn (University of Edinburgh / Johns Hopkins University)
Julia Kreutzer (Heidelberg University)
Varvara Logacheva (University of Sheffield)
Christof Monz (University of Amsterdam)
Matteo Negri (FBK)
Wmt Aw Integration Services
Aurélie Névéol (LIMSI, CNRS)Mariana Neves (Federal Institute for Risk Assessment / Hasso Plattner Institute)
Matt Post (Johns Hopkins University)
Stefan Riezler (Heidelberg University)
Artem Sokolov (Heidelberg University, Amazon Development Center, Berlin)
Continuous integration helps your team deliver updates to their customers faster and more frequently.
You can practice continuous integration on AWS in several ways.
Set up a continuous integration workflow with AWS CodePipeline, which lets you build a workflow that builds code in AWS CodeBuild every time you commit a change.
Home
This conference builds on a series of annual workshops and conferences on statistical machine translation, going back to 2006:
- the NAACL-2006 Workshop on Statistical Machine Translation,
- the ACL-2007 Workshop on Statistical Machine Translation,
- the ACL-2008 Workshop on Statistical Machine Translation,
- the EACL-2009 Workshop on Statistical Machine Translation,
- the ACL-2010 Workshop on Statistical Machine Translation
- the EMNLP-2011 Workshop on Statistical Machine Translation,
- the NAACL-2012 Workshop on Statistical Machine Translation,
- the ACL-2013 Workshop on Statistical Machine Translation,
- the ACL-2014 Workshop on Statistical Machine Translation,
- the EMNLP-2015 Workshop on Statistical Machine Translation,
- the First Conference on Machine Translation (at ACL-2016).
IMPORTANT DATES
| Release of training data for shared tasks | January/February, 2017 |
| Evaluation periods for shared tasks | April/May, 2017 |
| Paper submission deadline | June 9th, 2017 (Midnight, UTC -11) |
| Paper notification | June 30th, 2017 |
| Camera-ready version due | July 14th, 2017 |
| Conference in Copenhagen | September 7-8, 2017 |
OVERVIEW
This year's conference will feature the following shared tasks:
- a news translation task,
- a biomedical translation task ,
- an automatic post-editing task,
- a metrics task (assess MT quality given reference translation).
- a quality estimation task (assess MT quality without access to any reference),
- a multimodal translation task
- a task dedicated to the training of neural MT systems
- a task on bandit learning for MT
In addition to the shared tasks, the conference will also feature scientific papers on topics related to MT. Topics of interest include, but are not limited to:
- word-based, phrase-based, syntax-based, semantics-based SMT
- neural machine translation
- using comparable corpora for SMT
- incorporating linguistic information into SMT
- decoding
- system combination
- error analysis
- manual and automatic method for evaluating MT
- scaling MT to very large data sets
REGISTRATION
Registration will be handled by EMNLP 2017.NEWS TRANSLATION TASK
The first shared task which will examine translation between the following language pairs:
- English-Chinese and Chinese-English NEW
- English-Czech and Czech-English
- English-Finnish and Finnish-English
- English-German and German-English
- English-Latvian and Latvian-English NEW
- English-Russian and Russian-English
- English-Turkish and Turkish-English
All participants who submit entries will have their translations evaluated. We will evaluate translation performance by human judgment. To facilitate the human evaluation we will require participants in the shared tasks to manually judge some of the submitted translations. For each team, this will amount to ranking 300 sets of 5 translations, per language pair submitted.
BIOMEDICAL TRANSLATION TASK
In this second edition of this task, we will evaluate systems for the translation of biomedical documents for the following languages pairs:
- English-Czech and Czech-English NEW
- English-French and French-English
- English-German and German-English NEW
- English-Hungarian and Hungarian-English NEW
- English-Polish and Polish-English NEW
- English-Portuguese and Portuguese-English
- English-Romanian and Romanian-English NEW
- English-Spanish and Spanish-English
- English-Swedish and Swedish-English NEW
Parallel corpora will be available for all language pairs but also monoligual corpora for some languages. Evaluation will be carried out both automatically and manually.
AUTOMATIC POST-EDITING TASK
This shared task will examine automatic methods for correcting errors produced by machine translation (MT) systems. Automatic Post-editing (APE) aims at improving MT output in black box scenarios, in which the MT system is used 'as is' and cannot be modified.From the application point of view APE components would make it possible to:
- Cope with systematic errors of an MT system whose decoding process is not accessible
- Provide professional translators with improved MT output quality to reduce (human) post-editing effort
In this third edition of the task, the evaluation will focus on English-German (IT domain) and German-English (Medical domain)
METRICS TASK
The metrics task (also called evaluation task) will assess automatic evaluation metrics' ability to:
- Evaluate systems on their overall performance on the test set
- Evaluate systems on a sentence by sentence level
Participants in the shared evaluation task will use their automatic evaluation metrics to score the output from the translation task and the NMT training task. Dead zedclout games. In addition to MT outputs from the other two tasks, the participants will be provided with reference translations. We will measure the correlation of automatic evaluation metrics with the human judgments.
NEURAL MT TRAINING TASK (NMT TRAINING TASK)
This task will assess your team's ability to train a fixed neural MT model given fixed data.Participants in the NMT training task will be given complete a Neural Monkey configuration file which describes the neural model. Training and validation data with fixed pre-processing scheme will be also given (English-to-Czech and Czech-to-English translation).
The participants will be expected to submit the variables file, i.e. the trained neural network, for one or both of the translation directions. We will use the variables and a fixed revision of Neural Monkey to translate official WMT17 test set. The outputs of the various configurations of the system will be scored using the standard manual evaluation procedure.
BANDIT LEARNING TASK
Bandit Learning for MT is a framework to train and improve MT systems by learning from weak or partial feedback: Instead of a gold-standard human-generated translation, the learner only receives feedback to a single proposed translation (this is why it is called partial), in form of a translation quality judgement (which can be as weak as a binary acceptance/rejection decision).
In this task, the user feedback will be simulated by a service hosted on Amazon Web Services (AWS), where participants can submit translations and receive feedback and use this feedback for training an MT model (German-to-English, e-commerce). Reference translations will not be revealed at any point, also evaluations are done via the service. The goal of this task is to find systems that learn efficiently and effectively from this type of feedback, i.e. they learn fast and achieve high translation quality without references.
PAPER SUBMISSION INFORMATION
Submissions will consist of regular full papers of 6-10 pages, plus additional pages for references, formatted following the EMNLP 2017 guidelines. In addition, shared task participants will be invited to submit short papers (suggested length: 4-6 pages, plus references) describing their systems or their evaluation metrics. Both submission and review processes will be handled electronically. Note that regular papers must be anonymized, while system descriptions do not need to be.
Research papers that have been or will be submitted to other meetings or publications must indicate this at submission time, and must be withdrawn from the other venues if accepted and published at WMT 2017. We will not accept for publication papers that overlap significantly in content or results with papers that have been or will be published elsewhere. It is acceptable to submit work that has been made available as a technical report (or similar, e.g. in arXiv) without citing it. This double submission policy only applies to research papers, so system papers can have significant overlap with other published work, if it is relevant to the system description.
We encourage individuals who are submitting research papers to evaluate their approaches using the training resources provided by this conference and past workshops, so that their experiments can be repeated by others using these publicly available corpora.
POSTER FORMAT
A0 Landscape.ANNOUNCEMENTS
| Subscribe to to the announcement list for WMT by entering your e-mail address below. This list will be used to announce when the test sets are released, to indicate any corrections to the training sets, and to amend the deadlines as needed. |
| You can read past announcements on the Google Groups page for WMT. These alsoinclude an archive of announcements from earlier workshops. |
INVITED TALK
Holger Schwenk (Facebook)Multilingual Representions and Applications in NLP
ORGANIZERS
Ondřej Bojar (Charles University in Prague)Christian Buck (University of Edinburgh)
Rajen Chatterjee (FBK)
Christian Federmann (MSR)
Yvette Graham (DCU)
Barry Haddow (University of Edinburgh)
Matthias Huck (University of Edinburgh)
Antonio Jimeno Yepes (IBM Research Australia)
Philipp Koehn (University of Edinburgh / Johns Hopkins University)
Julia Kreutzer (Heidelberg University)
Varvara Logacheva (University of Sheffield)
Christof Monz (University of Amsterdam)
Matteo Negri (FBK)
Wmt Aw Integration Services
Aurélie Névéol (LIMSI, CNRS)Mariana Neves (Federal Institute for Risk Assessment / Hasso Plattner Institute)
Matt Post (Johns Hopkins University)
Stefan Riezler (Heidelberg University)
Artem Sokolov (Heidelberg University, Amazon Development Center, Berlin)
Lucia Specia (University of Sheffield)
Marco Turchi (FBK)
Karin Verspoor (University of Melbourne)
PROGRAM COMMITTEE
- Tim Anderson (Air Force Research Laboratory)
- Eleftherios Avramidis (German Research Center for Artificial Intelligence (DFKI))
- Daniel Beck (University of Melbourne)
- Arianna Bisazza (University of Amsterdam)
- Graeme Blackwood (IBM Research)
- Frédéric Blain (University of Sheffield)
- Ozan Caglayan (LIUM, Le Mans University)
- Marine Carpuat (University of Maryland)
- Francisco Casacuberta (Universitat Politècnica de València)
- Daniel Cer (Google)
- Mauro Cettolo (FBK)
- Rajen Chatterjee (Fondazione Bruno Kessler)
- Boxing Chen (NRC)
- Colin Cherry (NRC)
- David Chiang (University of Notre Dame)
- Eunah Cho (Karlsruhe Institute of Technology)
- Kyunghyun Cho (New York University)
- Vishal Chowdhary (MSR)
- Jonathan Clark (Microsoft)
- Marta R. Costa-jussà (Universitat Politècnica de Catalunya)
- Praveen Dakwale (University of Amsterdam)
- Steve DeNeefe (SDL Language Weaver)
- Michael Denkowski (Amazon.com, Inc.)
- Markus Dreyer (Amazon.com)
- Nadir Durrani (QCRI)
- Desmond Elliott (University of Edinburgh)
- Marzieh Fadaee (University of Amsterdam)
- Marcello Federico (FBK)
- Minwei Feng (IBM Watson Group)
- Yang Feng (Institute of Computing Technology, Chinese Academy of Sciences)
- Andrew Finch (NICT)
- Orhan Firat (Google Research)
- Marina Fomicheva (Universitat Pompeu Fabra)
- José A. R. Fonollosa (Universitat Politècnica de Catalunya)
- Mikel L. Forcada (Universitat d'Alacant)
- George Foster (National Research Council)
- Alexander Fraser (Ludwig-Maximilians-Universität München)
- Markus Freitag (IBM Research)
- Ekaterina Garmash (University of Amsterdam)
- Ulrich Germann (University of Edinburgh)
- Hamidreza Ghader (Informatics Institute, University of Amsterdam)
- Jesús González-Rubio (Universitat Politècnica de València)
- Cyril Goutte (National Research Council Canada)
- Thanh-Le Ha (Karlsruhe Institute of Technology)
- Nizar Habash (New York University Abu Dhabi)
- Jan Hajic (Charles University)
- Greg Hanneman (Carnegie Mellon University)
- Christian Hardmeier (Uppsala universitet)
- Eva Hasler (SDL)
- Yifan He (Bosch Research and Technology Center)
- Kenneth Heafield (University of Edinburgh)
- Carmen Heger (Iconic)
- John Henderson (MITRE)
- Felix Hieber (Amazon Research)
- Stéphane Huet (Université d'Avignon)
- Young-Sook Hwang (SKPlanet)
- Gonzalo Iglesias (SDL)
- Doug Jones (MIT Lincoln Laboratory)
- Marcin Junczys-Dowmunt (Adam Mickiewicz University, Poznań)
- Roland Kuhn (National Research Council of Canada)
- Shankar Kumar (Google)
- Ákos Kádár (Tilburg University)
- David Langlois (LORIA, Université de Lorraine)
- William Lewis (Microsoft Research)
- Qun Liu (Dublin City University)
- Shujie Liu (Microsoft Research Asia, Beijing, China)
- Saab Mansour (Apple)
- Daniel Marcu (ISI/USC)
- Arne Mauser (Google, Inc)
- Mohammed Mediani (Karlsruhe Institute of Technology)
- Abhijit Mishra (IBM Research India)
- Maria Nadejde (University of Edinburgh)
- Preslav Nakov (Qatar Computing Research Institute, HBKU)
- Jan Niehues (Karlsruhe Institute of Technology)
- Kemal Oflazer (Carnegie Mellon University - Qatar)
- Tsuyoshi Okita (Kyushuu institute of technology university)
- Daniel Ortiz-Martínez (Technical University of Valencia)
- Martha Palmer (University of Colorado)
- Siddharth Patwardhan (IBM Watson)
- Pavel Pecina (Charles University)
- Stephan Peitz (Apple)
- Sergio Penkale (Lingo24)
- Jan-Thorsten Peter (RWTH Aachen University)
- Maja Popović (Humboldt University of Berlin)
- Preethi Raghavan (IBM Research TJ Watson)
- Stefan Riezler (Heidelberg University)
- Baskaran Sankaran (IBM T.J. Watson Research Center)
- Jean Senellart (SYSTRAN)
- Rico Sennrich (University of Edinburgh)
- Wade Shen (MIT)
- Michel Simard (NRC)
- Patrick Simianer (Heidelberg University)
- Linfeng Song (University of Rochester)
- Sara Stymne (Uppsala University)
- Katsuhito Sudoh (Nara Institute of Science and Technology (NAIST))
- Felipe Sánchez-Martínez (Universitat d'Alacant)
- Aleš Tamchyna (Charles University in Prague, UFAL MFF)
- Jörg Tiedemann (University of Helsinki)
- Christoph Tillmann (IBM Research)
- Ke M. Tran (University of Amsterdam)
- Dan Tufiș (Research Institute for Artificial Intelligence, Romanian Academy)
- Marco Turchi (Fondazione Bruno Kessler)
- Ferhan Ture (Comcast Labs)
- Masao Utiyama (NICT)
- David Vilar (Amazon)
- Stephan Vogel (Qatar Computing Research Institute)
- Martin Volk (University of Zurich)
- Taro Watanabe (Google)
- Bonnie Webber (University of Edinburgh)
- Marion Weller-Di Marco (LMU München, Universität Stuttgart)
- Philip Williams (University of Edinburgh)
- Hua Wu (Baidu)
- Joern Wuebker (Lilt, Inc.)
- François Yvon (LIMSI/CNRS)
- Marlies van der Wees (University of Amsterdam)
ANTI-HARASSMENT POLICY
WMT follows the ACL's anti-harassment policy
CONTACT
Wmt Aw Integration Group
For general questions, comments, etc. please send email to bhaddow@inf.ed.ac.uk.
For task-specific questions, please contact the relevant organisers.
ACKNOWLEDGEMENTS
This conference has received fundingfrom the European Union's Horizon 2020 researchand innovation programme under grant agreements645452 (QT21) and 645357 (Cracker).Wmt Aw Integration Solutions
Wmt Aw Integration Inc
We thank Yandex for their donation of data for the Russian-English and Turkish-English news tasks, and the University of Helsinki for their donation for the Finnish-English news tasks.