Difference between revisions of "Task ideas for Google Code-in"

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{{Taskidea
 
 
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|type=quality
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|mentors=*
 
|tags=localisation
 
|tags=localisation
|title=html-tools localisation
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|title=Complete website localisation in a language not fully localised
 
|description=
 
|description=
 
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{{Taskidea
 
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|multi=200
 
|multi=200
 
|beginner=yes
 
|beginner=yes
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{{Taskidea
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|type=quality
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|mentors=mlforcada, anakuz, xavivars, fotonzade, sevilay, Unhammer, eirien, dolphingarlic, wei2912, marcriera, padth4i, Oguz, JNW, jjjppp
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|tags=xml, dictionaries
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|title=Identify and add 100 new entries to a bilingual dictionary
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|description=Our translation systems require large lexicons so as to provide production-quality coverage of any input data. This task requires the student to add 100 new words to a bidirectional dictionary. Choose one of the language pairs listed below, and with the help of your mentor, identify some text in one of the two languages, and run the text through Apertium's translator for that language pair to identify 100 unknown forms. As needed, add the stems of these forms to the individual languages' analysers in an appropriate way so that these words are analysed correctly. Your submission should be in the form of a pull request to each of the appropriate repositories on GitHub.<br/>The language pairs we can mentor for this task are the following: %ALLPAIRS%.<br/> [http://wiki.apertium.org/wiki/Task_ideas_for_Google_Code-in/Grow_bilingual More instructions for this task here]...
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|multi=40
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|beginner=yes
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}}
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{{Taskidea
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|type=quality
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|mentors=mlforcada, anakuz, xavivars, fotonzade, ftyers, sevilay, eirien, dolphingarlic, wei2912, marcriera, padth4i, Oguz, JNW
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|tags=xml, dictionaries
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|title=Identify and add 250 new entries to a bilingual dictionary
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|description=Our translation systems require large lexicons so as to provide production-quality coverage of any input data. This task requires the student to add 250 new words to a bidirectional dictionary. Choose one of the language pairs listed below, and with the help of your mentor, identify some text in one of the two languages, and run the text through Apertium's translator for that language pair to identify 250 unknown forms. As needed, add the stems of these forms to the individual languages' analysers in an appropriate way so that these words are analysed correctly. Your submission should be in the form of a pull request to each of the appropriate repositories on GitHub.<br/>The language pairs we can mentor for this task are the following: %ALLPAIRS%.<br/> [http://wiki.apertium.org/wiki/Task_ideas_for_Google_Code-in/Grow_bilingual More instructions for this task here]...
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|dup=20
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|beginner=no
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}}
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{{Taskidea
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|type=quality
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|mentors=fotonzade, JNW, ftyers, anakuz, xavivars, mlforcada, shardulc, sevilay, Unhammer, dolphingarlic, wei2912, marcriera
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|tags=xml, dictionaries
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|title=Post-edit 500 sentences of a public domain text
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|description=Many of our systems benefit from statistical methods used with (ideally public domain) bilingual data. For this task, you need to translate a public domain text using an available machine translation system (Apertium preferred) and clean up the translations yourself manually. Commit the post-edited texts (in plain text format) to an existing (via pull request) or if needed new github repository for the language pair in dev/ or texts/ folder. The texts are subject to mentor approval.<br/>The language pairs we can mentor for this task are the following: %ALLPAIRS%.
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|multi=10
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|beginner=yes
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}}
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{{Taskidea
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|type=research
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|mentors=khannatanmai, sevilay, padth4i
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|tags=evaluation
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|title=Evaluate an existing Apertium translation pair on a text
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|description= Pick an existing Apertium language pair and get a parallel text for that language pair. Translate the text using the Apertium translation pair and evaluate the translation using an automatic evaluation metric like BLEU and/or evaluate it manually.<br/>The language pairs we can mentor for this task are the following: %ALLPAIRS%.<br/>[http://wiki.apertium.org/wiki/Task_ideas_for_Google_Code-in/Evaluation_of_translation_of_an_existing_pair Read more]...
 
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Revision as of 04:46, 5 December 2019

This is the task ideas page for Google Code-in, here you can find ideas on interesting tasks that will improve your knowledge of Apertium and help you get into the world of open-source development.

The people column lists people who you should get in contact with to request further information. All tasks are 2 hours maximum estimated amount of time that would be spent on the task by an experienced developer, however:

  1. this is the time expected to take by an experienced developer, you may find that you spend more time on the task because of the learning curve.

Categories:

  • code: Tasks related to writing or refactoring code
  • documentation: Tasks related to creating/editing documents and helping others learn more
  • research: Tasks related to community management, outreach/marketting, or studying problems and recommending solutions
  • quality: Tasks related to testing and ensuring code is of high quality.
  • design: Tasks related to user experience research or user interface design and interaction

Clarification of "multiple task" types

  • multi = number of students who can do a given task (GCI's "max instances")
  • dup = number of times a student can do the same task

You can find descriptions of some of the mentors here.

Task ideas

The current task ideas here are for 2019. See Talk:Task ideas for Google Code-in for task ideas from previous years.

Mentors

These are languages that can be substituted for AAA and/or BBB for tasks each mentor is listed to mentor above.

If you do not see your language here, ask. We may be able to mentor or find you a mentor.

Mentor Languages
ftyers eng, spa, cat, fra, nor, rus, por, swe, tur, gag, aze
JNW eng, spa, fra, rus, tur, gag, aze, kaz, kir, kaa, tat, bak, kum, nog, kaa, uzb, uig, crh, khk, yid
anakuz grn, spa, por, rus
fotonzade eng, tur, aze, uig, tat, crh, kmr, ckb, fas
xavivars cat, spa, eng, fra
Unhammer nno, nob, swe, dan, fao, sme, ovd
shardulc eng, fra, mar, hin, urd, kan
m-alpha eng, fra, byv
popcorndude eng, spa, cym, heb
sevilay eng, ara, tur, kaz, aze, tat, gag, uig, uzb, crh, kum
eirien sah, rus, eng
khannatanmai eng, hin
flammie fin, krl, olo, hun, nio, kpv, mdf, tlh, fra, swe, eng, est, ekk, vro
dolphingarlic afr, deu, eng
wei2912 eng, zho
marcriera cat, spa, eng, ron
padth4i eng, mal, hin
Oguz eng, tur, uig, aze, crh
mlforcada eng, cat, eus, fra, por, glg, spa, gle, bre
ayushjain eng, hin
jjjppp eng, lat

Counts

Last updated by Firespeaker (talk) 07:30, 28 October 2019 (CET).

Category Count
code 16
documentation 4
research 11
quality 8
design 2
Total 33