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From Henri Yandell <bay...@apache.org>
Subject Re: [DISCUSS] Apache Joshua Incubator Proposal - Machine Translation Toolkit
Date Tue, 19 Jan 2016 03:57:05 GMT
Non-binding +1 to Joshua joining the Incubator. I'd be interested in
mentoring.


> -----Original Message-----
> From: jpluser <chris.a.mattmann@jpl.nasa.gov>
> Reply-To: "general@incubator.apache.org" <general@incubator.apache.org>
> Date: Tuesday, January 12, 2016 at 10:56 PM
> To: "general@incubator.apache.org" <general@incubator.apache.org>
> Cc: "post@cs.jhu.edu" <post@cs.jhu.edu>
> 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:
> https://wiki.apache.org/incubator/JoshuaProposal
> >
> >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.
> >
> >Thanks!
> >
> >Cheers,
> >Chris (Champion)
> >
> >———
> >
> >= Joshua Proposal =
> >
> >== Abstract ==
> >[[joshua-decoder.org|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
> >[[https://www.bing.com/translator|Bing Translator]] and
> >[[https://translate.google.com|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
> >computer.
> >
> >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
> >[[https://issues.apache.org/jira/browse/TIKA-1343|TIKA-1343]].
> >
> >== 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
> >translation.
> >
> >== 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
> >joshua-decoder.org. The source for the Joshua documentation is currently
> >hosted on Github at
> >https://github.com/joshua-decoder/joshua-decoder.github.com
> >
> >== Initial Source ==
> >Current source resides at Github: github.com/joshua-decoder/joshua (the
> >main decoder and toolkit) and github.com/joshua-decoder/thrax (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
> >statically.
> >
> >=== GNU GPL 2 ===
> > * Berkeley Aligner: https://code.google.com/p/berkeleyaligner/
> >
> >=== LGPG 2.1 ===
> > * KenLM: github.com/kpu/kenlm
> >
> >=== Apache 2.0 ===
> > * BerkeleyLM: https://code.google.com/p/berkeleylm/
> >
> >=== GNU GPL ===
> > * GIZA++: http://www.statmt.org/moses/giza/GIZA++.html
> >
> >== Required Resources ==
> > * Mailing Lists
> >   * private@joshua.incubator.apache.org
> >   * dev@joshua.incubator.apache.org
> >   * commits@joshua.incubator.apache.org
> >
> > * Git Repos
> >   * https://git-wip-us.apache.org/repos/asf/joshua.git
> >
> > * Issue Tracking
> >   * JIRA Joshua (JOSHUA)
> >
> > * Continuous Integration
> >   * Jenkins builds on https://builds.apache.org/
> >
> > * Web
> >   * http://joshua.incubator.apache.org/
> >   * wiki at http://cwiki.apache.org
> >
> >== 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 (post@cs.jhu.edu)
> > * Lewis John McGibbney (lewismc@apache.org)
> > * Chris Mattmann (mattmann@apache.org)
> >
> >== Affiliations ==
> >
> > * Johns Hopkins University
> >   * Matt Post
> >
> > * NASA JPL
> >   * 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
> >Email: chris.a.mattmann@nasa.gov
> >WWW:  http://sunset.usc.edu/~mattmann/
> >++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> >Adjunct Associate Professor, Computer Science Department
> >University of Southern California, Los Angeles, CA 90089 USA
> >++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
> >
> >
> >
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