Narimann/GSOC 2019 proposal: Kazakh-Turkish and Turkish-Kazakh

Contact Information

Name: Daniyar Nariman

Location: Kazan, Tatarstan

E-mail: n.daniyar@innopolis.ru, nariman9119@gmail.com

IRC: nariman

Github: https://github.com/nariman9119

Gitlab: https://gitlab.com/users/nariman9119

Telegram: nariman9119

Timezone: GMT+3, GMT+6

Skills

I am a third-year undergraduate student at Innopolis University(Tatarstan, Russia).

Major: Computer Science

Track: Data Science

Programming Skills: Python, Java, C, C++, Octave, XML

Languages

Russian - native

Kazakh - native

English - advanced(IELTS 6.0 - 2015)

Turkish - intermediate(5 years of studying in Kazakh-Turkish school)

NLP Related Projects

Word Sense Disambiguation for WordNet corpora

LSTM for Text classification

Russian-Tatar text classification

Tweet analysis on different preprocessing approaches

Keyboard layout and associated misspellings analysis

Dynamic Language Interpreter implementation

My current field of study is more related to Natural Language Processing. For the last 3 months, I worked on a company by developing a Labeller system, which can process the text and collect only the information needed.

Since I will graduate from University next year, I am planning to take the topic related to machine translation for my diploma, and this internship will help me a lot for deeper and more detailed study of how RBMT works.

Why is it that you are interested in Apertium?

I am studying Computer Science at my university, Data Science track. I am very interested in machine translation and other stuff related to the NLP.

I am interested in Apertium because it pays attention not only to common languages which have a lot of speakers around the world but also to these minority of languages which are not so popular and sometimes do not even have enough data to build a valuable translator.

Nowadays statistical machine translation(SMT) is very popular around the globe comparing with rule-based machine translation(RBMT). But the problem is that SMT requires a lot of data in the form of parallel languages corpora, since they very addicted to data, and many languages cannot afford it. While RBMT does not require so much data but requires a lot of effort to put in. From this point, we can conclude that Apertium is a good approach for machine translation purposes of small languages. Another point is that with a good and full implementation of a specific pair Apertium can reach accuracy comparable to big giants in this field such as Google or Yandex.

Which of the published tasks are you interested in? What do you plan to do?

Turkish to Kazakh and Tatar to Kazakh MT.

As we discussed in the Apertium-stuff mailing list, I will make these two pairs work stably in that direction, given my linguistic knowledge. Choosing from the given tasks, this task is more related to 1.2 and 1.4.

Proposal

Title

Turkish-Kazakh(Tatar-Kazakh) MT

Why Google and Apertium should sponsor it? How and who it will benefit in society?

There are a lot of people who speak these languages: Kazakh(around 11 million), Turkish(75 million), Tatar(5 million).

Turkish-Kazakh & Tatar-Kazakh pairs work stably, but only in one direction. So making them work in both direction will make these pairs more valuable and will lead to further development of Turkic languages in machine translation. In addition, it will help people to communicate with each other or at least translate the texts needed.

Work Plan

Post Application Period: Reading Wiki and Documentation

Community Bonding Period: Discuss details and get acquainted with all aspects of these pairs as far as possible.

Week 1

Start extending the Turkish monodix

Start extending the Kazakh monodix

Identification of morphological and syntax differences

Week 2

Complete and test Constraint Grammar rules from Turkish to Kazakh

Week 3

Supplement and test the Constraint Grammar rules from Turkish to Kazakh Test and debugging of constraint grammar rules

Week 4

Start to design and preliminary testing of transfer rules from Turkish to Kazakh Test and debugging of constraint grammar rules.

Deliverable 1

Complete set of constraint grammar rules for the tur-kaz direction

Part of transfer rules for the tur-kaz direction

Week 5

Design and preliminary testing of transfer rules from Turkish to Kazakh

Week 6

Design and preliminary testing of transfer rules from Turkish to Kazakh

Week 8

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Week 9

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Week 10

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Week 11

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Week 12

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List any non-Summer-of-Code plans you have for the Summer

I consider GSoC as a full-time job and I will not have other commitments during this time. Also, I am planning to start work on the project during the community bonding period to get acquainted with all aspects of these pairs as far as possible(20 hours a week).

I am planning to visit my parents in Kazakhstan in the period between 1-10 August. In this period I will be able to work at least 30 hours a week. During this time I will change timezone from GMT+3 to GMT+6.