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
* priv...@joshua.incubator.apache.org
* d...@joshua.incubator.apache.org
* comm...@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 (p...@cs.jhu.edu)
* Lewis John McGibbney (lewi...@apache.org)
* Chris Mattmann (mattm...@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.mattm...@nasa.gov
WWW: http://sunset.usc.edu/~mattmann/
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
Adjunct Associate Professor, Computer Science Department
University of Southern California, Los Angeles, CA 90089 USA
++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
