Intellego: Difference between revisions
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;Breaking closed ecosystems | ;Breaking closed ecosystems | ||
The current machine translation ecosystem is dominated by proprietary, closed systems. This includes their code base, their data collection processes, and public accessibility to their language resources. Additionally, the open MT ecosystem suffers from being unable to reach the vast majority of participants on the web through web services or APIs. | The current machine translation ecosystem is dominated by proprietary, closed systems. This includes their code base, their data collection processes, and public accessibility to their language resources. Additionally, the open MT ecosystem suffers from being unable to reach the vast majority of participants on the web through web services or APIs. | ||
;MT should not be a "one-size-fits-all" solution | |||
MT users are limited to using engines that follow a single MT methodology for all language pairs and content types. Studies have shown that a one-size-fits-all approach in MT does not provide the user with optimal translation output. Users need a single access point to different MT engines following different MT methodologies that will produce the best quality output by selecting the right engine for the right language pair. Intellego seeks to further establish an open MT ecosystem, as we feel it is the best way to accomplish this. | MT users are limited to using engines that follow a single MT methodology for all language pairs and content types. Studies have shown that a one-size-fits-all approach in MT does not provide the user with optimal translation output. Users need a single access point to different MT engines following different MT methodologies that will produce the best quality output by selecting the right engine for the right language pair. Intellego seeks to further establish an open MT ecosystem, as we feel it is the best way to accomplish this. | ||
;Machine translation in Firefox | ;Machine translation in Firefox | ||
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;Machine translation at Mozilla | ;Machine translation at Mozilla | ||
Many Mozilla l10n teams consist of only 1-2 people. While they would love to be able to provide l10n coverage for all of the Mozilla support sites (and other projects) they do not have the time or resources to accomplish the task. Users, thus, have a localized Firefox, but lack documented product support in their language. | Many Mozilla l10n teams consist of only 1-2 people. While they would love to be able to provide l10n coverage for all of the Mozilla support sites (and other projects) they do not have the time or resources to accomplish the task. Users, thus, have a localized Firefox, but lack documented product support in their language. | ||
;Advancements in MT research | ;Advancements in MT research | ||
Language support selection for machine translation projects are driven, in part, by ROI and availability of resources. This often results in minority languages, and even some majority languages (see Indic languages) being under-represented in the machine translation ecosystem. While ROI continues to be a primary motivator for incorporating support for these languages, they will remain under-represented and unsupported. | Language support selection for machine translation projects are driven, in part, by ROI and availability of resources. This often results in minority languages, and even some majority languages (see Indic languages) being under-represented in the machine translation ecosystem. While ROI continues to be a primary motivator for incorporating support for these languages, they will remain under-represented and unsupported. | ||
Revision as of 12:23, 25 July 2014
Intellego is a machine translation project for the benefit of Mozilla and the Open Web.
Project details
Intellego is a machine translation (MT) platform that seeks to unify existing open MT projects by providing a single API for engine developers and a unified web service that hosts a number of different language pairs/engines/implementations in the back end. The Intellego platform will allow users to select from a number of open MT engines based on the most prominent MT methodologies in order to find the best target MT output for their on-the-fly translation.
- Breaking closed ecosystems
The current machine translation ecosystem is dominated by proprietary, closed systems. This includes their code base, their data collection processes, and public accessibility to their language resources. Additionally, the open MT ecosystem suffers from being unable to reach the vast majority of participants on the web through web services or APIs.
- MT should not be a "one-size-fits-all" solution
MT users are limited to using engines that follow a single MT methodology for all language pairs and content types. Studies have shown that a one-size-fits-all approach in MT does not provide the user with optimal translation output. Users need a single access point to different MT engines following different MT methodologies that will produce the best quality output by selecting the right engine for the right language pair. Intellego seeks to further establish an open MT ecosystem, as we feel it is the best way to accomplish this.
- Machine translation in Firefox
Google's ability to provide users with automatic translation of web content using Google Translate attracts global users to the Chrome browser. Intellego aims to be to Firefox what Google Translate is to Chrome by powering the automatic translation feature within the browser.
- Machine translation at Mozilla
Many Mozilla l10n teams consist of only 1-2 people. While they would love to be able to provide l10n coverage for all of the Mozilla support sites (and other projects) they do not have the time or resources to accomplish the task. Users, thus, have a localized Firefox, but lack documented product support in their language.
- Advancements in MT research
Language support selection for machine translation projects are driven, in part, by ROI and availability of resources. This often results in minority languages, and even some majority languages (see Indic languages) being under-represented in the machine translation ecosystem. While ROI continues to be a primary motivator for incorporating support for these languages, they will remain under-represented and unsupported.
Explore the wiki for more details about the Intellego project's purpose and focus.
Project meetings
The Intellego team meets every week to discuss the progress of the project.
We also occasionally have sprint meetings, where we work on a particular aspect of the project for a long stretch of time.
For more information about meetings, see our meetings page.
Resources
Team
These are the members of our Intellego team, with a brief overview of their relevant skills:
- Jeff Beatty (gueroJeff) (gueroJeff)
- Team lead.
- Localization, organization, programming.
- Majken Connor (Kensie) (Kensie)
- Community outreach, evangelism.
- Gordon P. Hemsley (GPHemsley) (GPHemsley)
- Linguistics, programming, BCP 47 (language tags).
- Mekki MacAulay (mekki) (mekki)
- Strategic management, partnerships, grants, business collaboration, evangelism.
Discussion
- IRC: #intellego
- Newsgroup: mozilla.tools.l10n