User:GD/proposal

Contact information

Name: Evgenii Glazunov

Location: Moscow, Russia

University: NRU HSE, Moscow (National Research University Higher School of Economics), 3rd-year student

E-mail: glaz.dikobraz@gmail.com

IRC: G_D

Timezone: UTC+3

Github: https://github.com/dkbrz

Am I good enough?

Education: Bachelor's Degree in Fundamental and Computational Linguistics (2015-2019) at NRU HSE

Courses:

• Programming (Python, R, Flask, HTML, xml, Machine Learning)
• Morphology, Syntax, Semantics, Typology/Language Diversity
• Mathematics (Discrete Mathemathics, Linear Algebra and Calculus, Probability Theory, Mathematical Statistics, Computability and Complexity, Logic, Graphs and Topology, Theory of Algorithms)
• Latin, Latin in modern Linguistics, Ancient Literature

Languages: Russian (native), English (academic), French(A2-B1), Latin (a bit), German (A1)

Personal qualities: responsibility, punctuality, being hard-working, passion for programming, perseverance, resistance to stress

Why is it I am interested in machine translation? Why is it that I am interested in Apertium?

The speed of information circulation does not allow to spend time on human translation. I am truly interested in formal methods and models because they represent the way any language is constructed (as I see it). Despite some exceptions, in general language is very logical and the main problem is how to find proper systematic description. Apertium is a powerful platform that allows to build impressive rule-based engines. I think rule-based translation very promising if we provide enough data and an effective analysis

Which of the published tasks am I interested in? What do I plan to do?

I want to work on Graph dictionaries

Proposal

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

I think there is a lot of math in language and graph representation of dictionaries is an exciting idea, because it adds some kind of cross-validation and internal system source of information. This information help to fill some lacunae that appear while creating a dictionary. This will improve a quality of translation as we manage to expand bidix.

Graph representation is very promising because it represents a philosophical model of a metalanguage knowledge. Knowing several languages, I know that it could be hard to recall some rare word and it is easier to translate from French to English and only then to Russian - because I forgot the word-pair between Russian and French. This graph representation works just like my memory: we cannot recall what is this word from L1 in L2. Hmm, we know L1-L3 and L3-L2. Oh, that's the link we need. Now we know L1-L3 word-pair. So, as we work on natural language processing, let's use natural instruments and systems as well.

The main benefit of this project is reducing human labor and automatization of part of the dictionary development.

• Finding lacunae in created dictionary (what words are missing).
• Dictionary enrichment based on algorithm that offer variants and evaluation of these variants.
• A potential base for creating new pairs.

Coding Challenge

ipynb with current state of my coding challenge

Week by week work plan

Post application period

1. Refreshing and obtainig more specific knowledge about graph theory (during current course and in extra sources)

2. Thinking about statistical approach that can be relevant for this particular task

3. Theoretical research on general algorithmic optimisation

Community bonding period

1. Discussing my considerations and ideas with mentors

2. Icluding particularities and detail that are relevant

3. Correcting work plan according to new ideas

First phase

Week 1: Collecting data, preprocessing

Week 2: Experiments on small datasets with existing evaluation pairs (compare existing bidix with artificially created via graph)

Week 3: Error analysis and improvement ideas

Week 4: Improving code, preliminary running on medium data, first phase results, correcting plans

Second phase

Week 5: Optimization work based on medium-data experience

Week 6: Evaluating and improving metrics (experiments), estimate optimization

Week 7: Running on big data

Week 8: Finding errors and possible optimization, pre-results

Third phase

Week 9: Stable version on existing pairs, preprocessing of in-work pairs, experiments

Week 10: Final version of model, do the actual dictionary enrichment

Week 11: Evaluate results, estimate how much better dictionaries became

Week 12: Documentation, cleaning up the code

Final evaluation

Non-Summer-of-Code plans you have for the Summer

GSoC is the only project I have this summer. I have some exams in the end of June.