Difference between revisions of "User:Khannatanmai"
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[[Category:GSoC student proposals|Khannatanmai]]
Revision as of 14:54, 16 March 2019
Google Summer of Code 2019: Proposal [First Draft]
- 1 Personal Details
- 2 About Me
- 3 Project Proposal
- 4 Skills and Qualifications
- 5 Non-Summer-Of-Code Plans
Name: Tanmai Khanna
E-mail address: firstname.lastname@example.org , email@example.com
Time Zone: GMT+5:30
Open Source Softwares I use: I have used Apertium in the past, Ubuntu, Firefox, VLC.
Professional Interests: I’m currently studying NLP and I have a particular interest in Linguistics and NLP tools, specifically Machine Translation and its components.
Hobbies: I love Parliamentary Debating, Singing, and Reading.
What I want to get out of GSoC
I’ve studied about apertium and it’s amazing to me that I get an opportunity to work with them. NLP is what I want to do in life and working with a team to develop tools that actual people use will be invaluable experience that classes simply cannot match. Of course, the stipend is a big plus!
Why is it that I am interested in Apertium and Machine Translation?
Apertium is an Open Source Rule-based MT system. I have been part of the Machine Translation lab in my college and it interests me because it’s a complex problem and is often called NLP-Complete by my professors, i.e. it uses most of the tools NLP has to offer and hence if one learns to do good MT they learn most of Natural Language Processing.
Each part of Apertium's mission statement, especially the fact that they focus on Low Resource Languages, interests me and excites me to be working with them. While Neural Networks and Deep Learning is the fad these days, it only works for resource rich languages.
A tool which is rule based and open source really helps the community with language pairs that are resource poor and gives them free translations for their needs. I'm interested in working with Apertium and GSoC so that I can contribute to helping the community through the project.
Which of the published tasks am I interested in? What do I plan to do?
Anaphora Resolution - Apertium currently uses default male. I don’t plan to make a perfect anaphora resolution in 3 months, but I can make one which uses complex features to pick an antecedent. I am confident that it will increase the fluency and intelligibility of output significantly.
The Anaphora Resolution tool will be language agnostic and will improve the output for most language pairs in Apertium. Pronouns are present in a lot of languages and they change based on their antecedent's gender, number, person, etc. This tool will figure out the antecedent and will choose the correct pronoun accordingly.
Sentences like “The group agreed to release his mission statement”, "The girl ate his apple" are grammatically incoherent and an incorrect pronoun will more often than not confuse people in complex sentences.
What puts people off Machine Translation is the lack of fluency, and this tool will definitely generate more fluent sentences leading to more trust in this tool.
- Understand the system, Get familiar with the files that I need to modify
- Formalise the problem, limit the scope of anaphora resolution (To Anaphora needed for MT)
- May include general anaphora (Need to decide on scope [for gisting])
- Annotation of anaphora for evaluation
- Flowchart of proposed system and Pseudocode
- Implement a scoring system for antecedent indicators [work for Target Language:English for now]
- Decide on a definite context window
- Implement basic anaphora outside the pipeline (python)
- Implement basic transfer rules to see if final system will work
- A basic prototype of final system ready
- Port the basic prototype to C++ (All further coding to be done in C++)
- TEST the system
- Document the outline
- Implement system to work out all possible antecedents
- Add ability to give antecedents a score
- TEST basic sentences with single antecedents, Test the pipeline
- Deliverable #1: Anaphora Resolution for single antecedents, with transfer rules [The full pipeline]
- Implement Antecedent Indicators:
- Implement Boosting Indicators
- Implement Impeding Indicators
- Implement tie breaking systems
- Implement fallback for anaphora (in case of too many antecedents or not past certainty threshold)
- Code to remember antecedents for a certain window
- TEST Scoring System
- Implement transfer rules to deal with new additions
- Evaluate current system and produce precision and recall
- Implement Expectation-Maximization Algorithm
- Use Monolingual corpus to get probabilities of anaphora
- Implement choosing anaphora with max probability:
- If Scoring System has a tie
- As an independent system
- Compare the effectiveness of the above two possibilities
- Test EM Algorithm and implemented system
- Evaluate if addition of EM gives us significant benefits
- Deliverable #2: Anaphora Resolution with antecedent scores, fallback mechanism, EM algorithm
- Document Antecedent Indicators, Scoring System, EM Algorithm, Fallback
- Insert into Apertium pipeline
- Implement code to accept input in chunks and process it
- Output with anaphora attached
- EXTENSIVELY TEST final system with multiple pairs, see what needs to be changed for pairs
- TEST for backwards compatibility and ensure it
- Project Completed
- Agreement: Different for different languages?
- Agreement rules in Arabic, however, are different. For instance, a set of non- human items (animals, plants, objects) is referred to by a singular feminine pronoun.
- Since Arabic is an agglutinative language, the pronouns may appear as suffixes of verbs, nouns (e.g., in the case of possessive pronouns) and preposi- tions.
- Antecedent Indicators:
- Boosting Indicators[Scoring different for different languages]
- First NPs
- Indicating Verbs
- Lexical Reiteration
- Section Heading Preference
- Collocation Pattern Preference
- Immediate reference (if it is pronoun then its reference)
- Sequential Instructions
- Impeding Indicators
- Prepositional NPs
A description of how and who it will benefit in society
It will definitely benefit most users of Apertium and hopefully will attract more people to the tool. I’m from India and for a lot of our languages we don’t have the data to create reliable Neural MT systems. Similarly, for all resource poor languages, Apertium provides an easy and reliable MT system for their needs. That’s how Apertium benefits society already.
However, what discourages people from using Machine Translation is unintelligible outputs and too much post editing which makes it very time consuming and costly for them. While Apertium aims to make minimal errors, as of now it selects a default male pronoun and that leads to several unintelligible outputs. Fixing that and making the system more fluent and intelligible overall will encourage people to use Machine Translation and will reduce costs of time and money.
Reasons why Google and Apertium should sponsor it
I feel this project has a wide scope as it affects almost all language pairs and helps almost everyone using Apertium. A decent Anaphora Resolution will give the output an important boost in its fluency and intelligibility, not just for one language, but all of them.
It’s a project which has promising future prospects as well - apart from the fact that language specific features can be added to improve it, we’ll be doing anaphora resolution even for languages which don’t need it to pick the correct pronoun. Doing this will enable Apertium to do Gisting Translation in the future, for which Anaphora Resolution is essential.
With this project I aim to help the users of Apertium, I wish to become a regular contributor to Apertium and become equipped to do a lot more Open Source Development in the future for other organisations as well.
By funding this project, Google will help improve an important Open Source tool and promote Open Source Development. In a world of Proprietary softwares, this is an invaluable resource for society and supports innovation that everyone can benefit from.
Skills and Qualifications
I'm currently a third year student at IIIT Hyderabad where I'm studying Computational Linguistics. It is a dual degree where we study Computer Science, Linguistics, NLP and more. I've been interested in linguistics from the very beginning and due to the rigorous programming courses, I'm also adept at several programming languages like Python, C++, XML, Bash Scripting, etc. I'm skilled in writing Algorithms. Data Structures, and Machine Learning Algorithms as well.
Due to the focused nature of our course, I have worked in several projects, such as building Translation Memory, Detecting Homographic Puns, POS Taggers, Grammar and Spell Checkers, Named Entity Recognisers, Building Chatbots, etc. all of which required a working understanding of Natural Language Processing.
I am fluent in English, Hindi and have basic knowledge of Spanish.
The details of my skills and work experience can be found here: CV
I successfully completed the coding challenge and proposed an alternate method to process the input as a chunk which resulted in a speedup of more than 2x.
The repo can be found at: https://github.com/khannatanmai/apertium
Files in Repo:
- Code to do basic anaphora resolution (last seen noun), input taken as a stream (byte by byte)
- Code to do basic anaphora resolution (last seen noun), input taken as a stream (as a chunk)
- Speed-Up Report
I will have my college vacations during GSoC so will have no other commitments in that period and will be dedicated to GSoC full time, i.e. 40 hours a week.
I am planning to go on a short trip to London from 18 May to 25 May but I will have internet there and will be working a little less than normal but will catch up.