Machine translation language divergence and lexical resources
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Machine Translation, Language Divergence and Lexical Resources. Pushpak Bhattacharyya Computer Science and Engineering Department IIT Bombay. Acknowledgement. NLP-AI members, CSE Dept, IIT Bombay. What is MT. Conversion of source language text to target language text. Computer Program.

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Machine Translation, Language Divergence and Lexical Resources

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Machine Translation, Language Divergence and Lexical Resources

Pushpak Bhattacharyya

Computer Science and Engineering Department

IIT Bombay


  • NLP-AI members, CSE Dept, IIT Bombay.

What is MT

Conversion of source language text to target language text

Computer Program

Document in L2

Document in L1

Kinds of MT Systems(How much of Human Participation)

  • Fully Automatic

  • Semi Automatic

    • Human Aided MT (HAMT)

      • Pre-editing

      • Post-editing


    • Machine Aided HT (MAHT)

      • On-line Dictionaries

      • Terminology Data Banks

      • Translation Memories


Kinds of MT Systems(domain coverage)

  • General Purpose

    (SYSTRAN in Europe)

  • Domain Specific

    (Tom-Mateo in Canada;

    Translates weather reports between

    French and English)

Kinds of MT Systems(point of entry from source to the target text)fwd

Why is MT difficult?Classical NLP problems

  • Ambiguity

    • Lexical

    • Structural

  • Ellipsis

  • Co-reference

    • Anaphora

    • Hypernymic


Why is MT DifficultLanguage Divergence

  • Lexico-Semantic Divergence

  • Structural Divergence

Language Divergence(English Hindi: Noun to Adjective)

  • The demands on sportsmen today can lead to burnout at an early age.

    (noun – the state of being extremely tired or ill, either physically or mentally, because you have worked too hard)

  • खिलाड़यों से जो आज अपेक्षाएं हैं, वे उन्हें कम उम्र में ही अक्रियाशील कर सकती हैं।

Language Divergence(English Hindi: Noun to Verb)

  • Every concert they gave us was a sell-out.

    (an event for which on the tickets have been sold)

  • उनके हर संगीत-कार्यक्रम के सभी टिकट बिक गए थे।

Language Divergence(English Hindi: Adjective to Adverb)

  • The children watched in wide-eyed amazement.

    (with eyes fully open because of fear, great surprise, etc)

  • बच्चे आश्चर्य से आँखें फाड़े देख रहे थे।

Language Divergence(English Hindi: Adjective to Verb)

  • He was in a bad mood at breakfast and wasn't very communicative.

    (able and willing to talk and give information to other people)

  • नाश्ते के समय वह खराब मूड में था और ज्यादा बात-चीत नहीं कर रहा था।

Language Divergence(English Hindi: Preposition to Adverb)

  • It gets cooler toward evening.

    (near a point in time)

  • शाम होते-होते ठंडक बढ़ जाती है।

Language Divergence(English Hindi: idiomatic usage)

  • Given her interest in children, teaching seems the right job for her.

    (when you consider sth)

  • बच्चों के प्रति (में) उसकी दिलचस्पी देखते हुए, अध्यापन उसके लिए उचित लगता है।

Language Divergence(Marathi-Hindi-English: case marking and postpositions transfer: works!)

  • प्रथम ताख्यात

  • वर्तमान(simple present)

    • तो जातो.

    • वह जाता है।

    • He goes.

  • स्थिरसत्य(universal truth)

    • पृथ्वी सूर्याभोवती फिरते.

    • पृथ्वी सूर्य के चारों ओर घूमती है।

    • The earth revolves round the sun.

Language Divergence(Marathi-Hindi-English: case marking and postpositions: works again!)

  • ऐतिहासिक सत्य(historical truth)

    • कृष्ण अर्जुनास सांगतो...

    • कृष्ण अर्जुन से कहते हैं...

    • Krushna says to Arjuna…

  • अवतरण (quoting)

    • दामले म्हणतात, ...

    • दामले कहते हैं, ...

    • Damle says,...

Language Divergence(Marathi-Hindi-English: case marking and postpositions: does not work!)

  • संनिहित भूत (immediate past)

    • कधी आलास? हा येतो इतकाच !

    • कब आये? बस अभी आया ।

    • When did you come? Just now (I came).

  • निःसंशय भविष्य (certainty in future)

    • आता तो मार खातो खास !

    • अब वह मार खायगा ही !

    • He is in for a thrashing.

  • आश्वासन (assurance)

    • मी तुम्हाला उद्या भेटतो.

    • मैं आप से कल मिलता हूँ।

    • I will see you tomorrow.

Language Divergence Theory: Lexico-Semantic Divergences

  • Conflational divergence

  • Structural divergence

  • Categorial divergence

  • Head swapping divergence

  • Lexical divergence

Language Divergence Theory: Syntactic Divergences

  • Constituent Order divergence

  • Adjunction Divergence

  • Preposition-Stranding divergence

  • Null Subject Divergence

  • Pleonastic Divergence

MT approaches

interlingua Based


Transfer Based

Vaquiouse Triangle

Interlingua Methodology

Directly obtain the meaning of the source sentence.

Do target sentence generation from the meaning


John gave the book to Mary.

Meaning representation:


agent: John

object: the book

receiver: Mary

ATLAS system in Fujitsu

precursor to

World wide project on UNL

Competing approaches


Transfer based

Direct approach

  • Word replacements

    I like mangoes

    maOM AcCa laga Aama

    I like (root) mangoes

  • Morphology

    maOM AcCa lagata Aama

    I like mangoes

  • Syntactic re-arrangement

    maOM Aama AcCa lagata hO

    I mangoes like

  • Idiomatization

    mauJao Aama AcCa lagata hO

    I (dative) mangoes like

Transfer Based

Source sentence processed for parsing, chunking etc.









Transfer Based

Transfer structures obtained for the target sentence.









Transfer Based

Morphology and language specific modifications







AcCa lagataa hO



Relation Between the Transfer and the Interlingua Models

Source language

Parse tree

Target Language

Parse tree

Interpretation generation


Parsing generation

Target language


source language


State of Affairs

  • Systran reports 19 different language


  • Only 8 alright for intended use.

  • Even fewer are capable of quality written

    or spoken text translation.

Notable Systems in India

  • Anusaaraka (IITK and IIIT Hyderabad: information access: one of the earliest systems)

  • Angla-Hindi (IITK: Transfer Based)

  • Shakti and Shiva (IIIT Hyderabad: Use of simple modules to create complex and high level performance)

  • UNL Based system (IIT Bombay- part of the UN effort: emphasis on semantics)

  • Hindi-Tamil system (AU-KBC, Chennai: based on the approach at IIIT Hyderabad)

Semantics: use of Lexical Resources

  • WordNet

  • Word Sense Disambiguation


  • A lexical knowledgebase based on conceptual lookup

    • Organizing concepts in a semantic network.

  • Organize lexical information in terms of word meaning, rather than word form

    • Wordnet can also be used as a thesaurus.

Lexical Matrix

The Structure of Hindi Wordnet

  • 30,000 unique words

  • 13,000 synsets

  • Wordnet Relations

    1. Lexical Relations (between word forms)



    2. Semantic Relations (between word meanings)




A small part of Hindi Wordnet

findtheinfo getindex

in_wn index_lookup read_synset


free_index morphstr

Hindi Data

Hindi WordNet APIs

The Hindi WSD System

Approach to WSD ….

Hindi Wordnet

Hindi Document



Context Bag Semantic Bag

WSD Algorithm

  • For a polysemous word w needing diambiguation, a set of context

  • words in its surrounding window is collected. Let this collection be C, the context bag. The window is the current sentence and the preceding and the following sentences.

  • For each sense s of w, do the following

    Let B be the bag of words obtained from the

    • Synonyms in the synsets

    • Glosses of the synsets

    • Example Sentences of the synsets

    • Hypernyms (recursively upto the roots)

    • Glosses of Hypernyms

    • Example Sentences of Hypernyms

WSD Algorithm (continued)

  • Hyponyms

  • Glosses of Hypernyms (recursively upto the leaves)

  • Example Sentences of Hyponyms

  • Meronyms (recursively upto the beginner synset)

  • Glosses of Meronyms

  • Example sentences of meronyms

  • Mesure the overlap between C and B using intersection similarity

  • Output that sense as the winner sense which has the maximum overlap simialrity value

  • Evaluation

    • Only Nouns

    • Test corpora from CIIL, Mysore.

    • Corpus from 8 domains, each containing around 2000 words on an average.


    Conclusions(Knowledge Based MT)

    • Language Divergence is the bottleneck

    • Not only for languages from distant families (English-Japanese)

    • But also for siblings within a family (Hindi-Marathi)

    • Solution lies in creating and exploiting knowledge structures

    Conclusions(Statistical MT)

    • Complementary (not really competing) approach

    • Example: IBM approach to translation from/to English and other languages (French, Chinese, and currently Hindi)

    • Needs vast amount of text aligned corpora

    • Basic idea is to maximize P(T|S) over all target sentences T: needs language modeling(P(T)) and translation modeling(P(S|T))

    Pre Editing

    The inspection team appointed by the United Nations visited Iraq early July, 2003.

    The <cnp> inspection team </cnp> {which was} appointed by the <org> United Nations </org> visited Iraq {in} early <date>July, 2003</date>.

    Post Editing

    • back (I want to eat well today)

    MMmaOM Aaja AcCa Kanaa caahta hUM

    mauJao Aaja AcCa Kanaa caaihe

    Terminology DB and Translation Memory

    • Special lexicon containing the domain terms and their translations

      • Nuclear Energy- AaNaivak }jaa-

    • Memories of previous translations

      • Apply fragments of previous translations to new translation situations


      • He bought a pen

      • ]snanao ek klama KrIda

      • All ministers have huge houses

      • saBaI pMtaoMko pasa bahut baDo Gar hOM


      • He bought a huge house

      • ]snanao ek bahut baDa Gar KrIda

    Pitfall of Translation Memory

    • German:

      Ein messer ist im schrank; er miβt eletrizitat.

      • TM1: Ein messer ist im schrank ->

        A meter is in the cabinet.

      • TM2: er miβt eletrizitat.

        It measures electricity

  • New situation

    Ein messer ist im schrank; er ist sehr scharf.

    • A meter is in the cabinet; it is very sharp (?).

    • Messer in German: Meter/Knife in English.


  • Ambiguity


    Co-reference Resolution

    • Pronoun

      • Sequence of commands to a robot:

        • place the wrench on the table.

        • Then paint it.

          • What does it refer to? (anaphora- back reference)

        • Learning of his intentions, Shivaji went to meet Afjal Khan, prepared with concealed weapons

          • Who does his refer to? (cataphora- forward ref)

    • Hypernymic

      • Children love to see lions? These animals, however, are getting extinct.


    Sequence of command to the Robot:

    Move the table to the corner.

    Also the chair.

    Second command needs completing by using the first part of the previous command.


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