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Machine Translation from English to HindiMarathi

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Title: Machine Translation from English to HindiMarathi


1
Machine Translation from English to Hindi/Marathi
  • Proposers Jayprasad J. Hegde and Ananthakrishnan
    Ramanathan
  • jjhegde,anand_at_cdacmumb
    ai.in
  • Team headed by Dr. M. Sasikumar
  • Team Size 7
  • Name of the company CDAC Mumbai (formerly NCST)
  • http//www.cdacmumbai.in/
  • Language/Language pair English to Hindi/Marathi.
  • What is the expected category of contribution
    Framework for translation from English to
    languages in the North-Western belt (Hindi,
    Marathi, Gujarati, etc.) Hindi generation,
    Transliteration components

2
Strength of the organization
  • Technical Capabilities
  • Strong RD background in areas such as AI, NLP,
    IR, Graphics, Networking, Databases, and Software
    Engineering
  • RD Budget
  • 11 Crores per annum
  • Decision Process
  • Through committees and approval of ED and DG
  • Manpower Involved
  • 150 regular scientists 90 project engineers
  • Some previous collaborations with
    universities/RD institutions
  • Fraunhoffer Institute, Germany,
  • University of Pennsylvania,
  • Indian Institute of Technology, Bombay,
  • Concordia University, Canada, etc.
  • SNDT
  • Goa University

3
Current and Previous Work
  • MaTra2 An English-Hindi Fully-automatic Machine
    Translation System (on-going)
  • MaTra A Human-aided Machine Translation System
    from English to Hindi
  • Setu A Hindi-English Cross Language Information
    Retrieval System (on-going)
  • Completed Masters work in Transliteration
    (English-Hindi, Hindi-English) and EBMT
  • On-going Doctoral work in Statistical MT
  • On-going Masters work in idiom discovery, clause
    boundary detection, abbreviation detection, and
    translation memory

4
Previous Work (contd.)
  • Older Projects
  • (not in chronological order)
  • Vaakya
  • Khoj An IR system
  • narch An news archive for PTI allowing access to
    news snippets

5
Architecture of existing/proposed MT system
6
Goals of our MT system
  • Understandable translations for wide-coverage
    rather than perfect translations for a small
    range (domain)
  • Work well for incomplete and ill-formed sentences
  • Example application To provide indicative
    translation for documents on the Web

7
Select features of the existing system
  • Structural and Lexical Transfer
  • Rule-bases for Hindi generation
  • Target language independent intermediate
    structured representation
  • Machine learning technique for transliteration
  • Transliteration (English Hindi, existing 80
    accuracy)
  • Tested approach for Hindi
  • 60 acceptability on unseen sentences from
    general news corpus capturing a wide variety of
    sentences

8
Summary
  • General Framework for MT. To be demonstrated for
    a language in the north-western belt Marathi.
  • Ongoing work in focused problems in MT
  • Transliteration
  • IL generation
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