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From Ashish <>
Subject Re: [VOTE] Accept Joshua as an Apache Incubator Podling
Date Sat, 30 Jan 2016 20:05:09 GMT
+ (non-binding)

On Sat, Jan 30, 2016 at 12:00 PM, Mattmann, Chris A (3980)
<> wrote:
> Hi Everyone,
> OK the discussion is now completed. Please VOTE to accept Joshua
> into the Apache Incubator. I’ll leave the VOTE open for at least
> the next 72 hours, with hopes to close it next Friday the 5th of
> February, 2016.
> [ ] +1 Accept Joshua as an Apache Incubator podling.
> [ ] +0 Abstain.
> [ ] -1 Don’t accept Joshua as an Apache Incubator podling because..
> Of course, I am +1 on this. Please note VOTEs from Incubator PMC
> members are binding but all are welcome to VOTE!
> Cheers,
> Chris
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> Chris Mattmann, Ph.D.
> Chief Architect
> Instrument Software and Science Data Systems Section (398)
> NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
> Office: 168-519, Mailstop: 168-527
> Email:
> WWW:
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> Adjunct Associate Professor, Computer Science Department
> University of Southern California, Los Angeles, CA 90089 USA
> ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> -----Original Message-----
> From: jpluser <>
> Date: Tuesday, January 12, 2016 at 10:56 PM
> To: "" <>
> Cc: "" <>
> Subject: [DISCUSS] Apache Joshua Incubator Proposal - Machine Translation
> Toolkit
>>Hi Everyone,
>>Please find attached for your viewing pleasure a proposed new project,
>>Apache Joshua, a statistical machine translation toolkit. The proposal
>>is in wiki draft form at:
>>Proposal text is copied below. I’ll leave the discussion open for a week
>>and we are interested in folks who would like to be initial committers
>>and mentors. Please discuss here on the thread.
>>Chris (Champion)
>>= Joshua Proposal =
>>== Abstract ==
>>[[|Joshua]] is an open-source statistical machine
>>translation toolkit. It includes a Java-based decoder for translating with
>>phrase-based, hierarchical, and syntax-based translation models, a
>>Hadoop-based grammar extractor (Thrax), and an extensive set of tools and
>>scripts for training and evaluating new models from parallel text.
>>== Proposal ==
>>Joshua is a state of the art statistical machine translation system that
>>provides a number of features:
>> * Support for the two main paradigms in statistical machine translation:
>>phrase-based and hierarchical / syntactic.
>> * A sparse feature API that makes it easy to add new feature templates
>>supporting millions of features
>> * Native implementations of many tuners (MERT, MIRA, PRO, and AdaGrad)
>> * Support for lattice decoding, allowing upstream NLP tools to expose
>>their hypothesis space to the MT system
>> * An efficient representation for models, allowing for quick loading of
>>multi-gigabyte model files
>> * Fast decoding speed (on par with Moses and mtplz)
>> * Language packs — precompiled models that allow the decoder to be run as
>>a black box
>> * Thrax, a Hadoop-based tool for learning translation models from
>>parallel text
>> * A suite of tools for constructing new models for any language pair for
>>which sufficient training data exists
>>== Background and Rationale ==
>>A number of factors make this a good time for an Apache project focused on
>>machine translation (MT): the quality of MT output (for many language
>>pairs); the average computing resources available on computers, relative
>>to the needs of MT systems; and the availability of a number of
>>high-quality toolkits, together with a large base of researchers working
>>on them.
>>Over the past decade, machine translation (MT; the automatic translation
>>of one human language to another) has become a reality. The research into
>>statistical approaches to translation that began in the early nineties,
>>together with the availability of large amounts of training data, and
>>better computing infrastructure, have all come together to produce
>>translations results that are “good enough” for a large set of language
>>pairs and use cases. Free services like
>>[[|Bing Translator]] and
>>[[|Google Translate]] have made these services
>>available to the average person through direct interfaces and through
>>tools like browser plugins, and sites across the world with higher
>>translation needs use them to translate their pages through automatically.
>>MT does not require the infrastructure of large corporations in order to
>>produce feasible output. Machine translation can be resource-intensive,
>>but need not be prohibitively so. Disk and memory usage are mostly a
>>matter of model size, which for most language pairs is a few gigabytes at
>>most, at which size models can provide coverage on the order of tens or
>>even hundreds of thousands of words in the input and output languages. The
>>computational complexity of the algorithms used to search for translations
>>of new sentences are typically linear in the number of words in the input
>>sentence, making it possible to run a translation engine on a personal
>>The research community has produced many different open source translation
>>projects for a range of programming languages and under a variety of
>>licenses. These projects include the core “decoder”, which takes a model
>>and uses it to translate new sentences between the language pair the model
>>was defined for. They also typically include a large set of tools that
>>enable new models to be built from large sets of example translations
>>(“parallel data”) and monolingual texts. These toolkits are usually built
>>to support the agendas of the (largely) academic researchers that build
>>them: the repeated cycle of building new models, tuning model parameters
>>against development data, and evaluating them against held-out test data,
>>using standard metrics for testing the quality of MT output.
>>Together, these three factors—the quality of machine translation output,
>>the feasibility of translating on standard computers, and the availability
>>of tools to build models—make it reasonable for the end users to use MT as
>>a black-box service, and to run it on their personal machine.
>>These factors make it a good time for an organization with the status of
>>the Apache Foundation to host a machine translation project.
>>== Current Status ==
>>Joshua was originally ported from David Chiang’s Python implementation of
>>Hiero by Zhifei Li, while he was a Ph.D. student at Johns Hopkins
>>University. The current version is maintained by Matt Post at Johns
>>Hopkins’ Human Language Technology Center of Excellence. Joshua has made
>>many releases with a list of over 20 source code tags. The last release of
>>Joshua was 6.0.5 on November 5th, 2015.
>>== Meritocracy ==
>>The current developers are familiar with meritocratic open source
>>development at Apache. Apache was chosen specifically because we want to
>>encourage this style of development for the project.
>>== Community ==
>>Joshua is used widely across the world. Perhaps its biggest (known)
>>research / industrial user is the Amazon research group in Berlin. Another
>>user is the US Army Research Lab. No formal census has been undertaken,
>>but posts to the Joshua technical support mailing list, along with the
>>occasional contributions, suggest small research and academic communities
>>spread across the world, many of them in India.
>>During incubation, we will explicitly seek to increase our usage across
>>the board, including academic research, industry, and other end users
>>interested in statistical machine translation.
>>== Core Developers ==
>>The current set of core developers is fairly small, having fallen with the
>>graduation from Johns Hopkins of some core student participants. However,
>>Joshua is used fairly widely, as mentioned above, and there remains a
>>commitment from the principal researcher at Johns Hopkins to continue to
>>use and develop it. Joshua has seen a number of new community members
>>become interested recently due to a potential for its projected use in a
>>number of ongoing DARPA projects such as XDATA and Memex.
>>== Alignment ==
>>Joshua is currently Copyright (c) 2015, Johns Hopkins University All
>>rights reserved and licensed under BSD 2-clause license. It would of
>>course be the intention to relicense this code under AL2.0 which would
>>permit expanded and increased use of the software within Apache projects.
>>There is currently an ongoing effort within the Apache Tika community to
>>utilize Joshua within Tika’s Translate API, see
>>== Known Risks ==
>>=== Orphaned products ===
>>At the moment, regular contributions are made by a single contributor, the
>>lead maintainer. He (Matt Post) plans to continue development for the next
>>few years, but it is still a single point of failure, since the graduate
>>students who worked on the project have moved on to jobs, mostly in
>>industry. However, our goal is to help that process by growing the
>>community in Apache, and at least in growing the community with users and
>>participants from NASA JPL.
>>=== Inexperience with Open Source ===
>>The team both at Johns Hopkins and NASA JPL have experience with many OSS
>>software projects at Apache and elsewhere. We understand "how it works"
>>here at the foundation.
>>== Relationships with Other Apache Products ==
>>Joshua includes dependences on Hadoop, and also is included as a plugin in
>>Apache Tika. We are also interested in coordinating with other projects
>>including Spark, and other projects needing MT services for language
>>== Developers ==
>>Joshua only has one regular developer who is employed by Johns Hopkins
>>University. NASA JPL (Mattmann and McGibbney) have been contributing
>>lately including a Brew formula and other contributions to the project
>>through the DARPA XDATA and Memex programs.
>>== Documentation ==
>>Documentation and publications related to Joshua can be found at
>> The source for the Joshua documentation is currently
>>hosted on Github at
>>== Initial Source ==
>>Current source resides at Github: (the
>>main decoder and toolkit) and (the grammar
>>extraction tool).
>>== External Dependencies ==
>>Joshua has a number of external dependencies. Only BerkeleyLM (Apache 2.0)
>>and KenLM (LGPG 2.1) are run-time decoder dependencies (one of which is
>>needed for translating sentences with pre-built models). The rest are
>>dependencies for the build system and pipeline, used for constructing and
>>training new models from parallel text.
>>Apache projects:
>> * Ant
>> * Hadoop
>> * Commons
>> * Maven
>> * Ivy
>>There are also a number of other open-source projects with various
>>licenses that the project depends on both dynamically (runtime), and
>>=== GNU GPL 2 ===
>> * Berkeley Aligner:
>>=== LGPG 2.1 ===
>> * KenLM:
>>=== Apache 2.0 ===
>> * BerkeleyLM:
>>=== GNU GPL ===
>> * GIZA++:
>>== Required Resources ==
>> * Mailing Lists
>>   *
>>   *
>>   *
>> * Git Repos
>>   *
>> * Issue Tracking
>>   * JIRA Joshua (JOSHUA)
>> * Continuous Integration
>>   * Jenkins builds on
>> * Web
>>   *
>>   * wiki at
>>== Initial Committers ==
>>The following is a list of the planned initial Apache committers (the
>>active subset of the committers for the current repository on Github).
>> * Matt Post (
>> * Lewis John McGibbney (
>> * Chris Mattmann (
>>== Affiliations ==
>> * Johns Hopkins University
>>   * Matt Post
>>   * Chris Mattmann
>>   * Lewis John McGibbney
>>== Sponsors ==
>>=== Champion ===
>> * Chris Mattmann (NASA/JPL)
>>=== Nominated Mentors ===
>> * Paul Ramirez
>> * Lewis John McGibbney
>> * Chris Mattmann
>>== Sponsoring Entity ==
>>The Apache Incubator
>>Chris Mattmann, Ph.D.
>>Chief Architect
>>Instrument Software and Science Data Systems Section (398)
>>NASA Jet Propulsion Laboratory Pasadena, CA 91109 USA
>>Office: 168-519, Mailstop: 168-527
>>Adjunct Associate Professor, Computer Science Department
>>University of Southern California, Los Angeles, CA 90089 USA


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