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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

Machine Translation, Language Divergence and Lexical Resources

Pushpak Bhattacharyya

Computer Science and Engineering Department

IIT Bombay

acknowledgement
Acknowledgement
  • NLP-AI members, CSE Dept, IIT Bombay.
what is mt
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
Kinds of MT Systems(How much of Human Participation)
  • Fully Automatic
  • Semi Automatic
    • Human Aided MT (HAMT)
      • Pre-editing
      • Post-editing

example

    • Machine Aided HT (MAHT)
      • On-line Dictionaries
      • Terminology Data Banks
      • Translation Memories

example

kinds of mt systems domain coverage
Kinds of MT Systems(domain coverage)
  • General Purpose

(SYSTRAN in Europe)

  • Domain Specific

(Tom-Mateo in Canada;

Translates weather reports between

French and English)

why is mt difficult classical nlp problems
Why is MT difficult?Classical NLP problems
  • Ambiguity
    • Lexical
    • Structural
  • Ellipsis
  • Co-reference
    • Anaphora
    • Hypernymic

examples

why is mt difficult language divergence
Why is MT DifficultLanguage Divergence
  • Lexico-Semantic Divergence
  • Structural Divergence
language divergence english hindi noun to adjective
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
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
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
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
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
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
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
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
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
Language Divergence Theory: Lexico-Semantic Divergences
  • Conflational divergence
  • Structural divergence
  • Categorial divergence
  • Head swapping divergence
  • Lexical divergence
language divergence theory syntactic divergences
Language Divergence Theory: Syntactic Divergences
  • Constituent Order divergence
  • Adjunction Divergence
  • Preposition-Stranding divergence
  • Null Subject Divergence
  • Pleonastic Divergence
mt approaches
MT approaches

interlingua Based

Direct

Transfer Based

Vaquiouse Triangle

interlingua methodology
Interlingua Methodology

Directly obtain the meaning of the source sentence.

Do target sentence generation from the meaning

representation.

John gave the book to Mary.

Meaning representation:

give-action:

agent: John

object: the book

receiver: Mary

ATLAS system in Fujitsu

precursor to

World wide project on UNL

competing approaches
Competing approaches

Direct

Transfer based

direct approach
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
Transfer Based

Source sentence processed for parsing, chunking etc.

S

VP

NP

V

NP

I

like

mangoes

transfer based1
Transfer Based

Transfer structures obtained for the target sentence.

S

VP

NP

NP

V

I

mangoes

like

transfer based2
Transfer Based

Morphology and language specific modifications

S

VP

NP

NP

V

mauJao

AcCa lagataa hO

Aama

relation between the transfer and the interlingua models
Interlingua

Relation Between the Transfer and the Interlingua Models

Source language

Parse tree

Target Language

Parse tree

Interpretation generation

transfer

Parsing generation

Target language

words

source language

words

state of affairs
State of Affairs
  • Systran reports 19 different language

pairs.

  • Only 8 alright for intended use.
  • Even fewer are capable of quality written

or spoken text translation.

notable systems in india
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
Semantics: use of Lexical Resources
  • WordNet
  • Word Sense Disambiguation
wordnet
Wordnet
  • 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.
the structure of hindi wordnet
The Structure of Hindi Wordnet
  • 30,000 unique words
  • 13,000 synsets
  • Wordnet Relations

1. Lexical Relations (between word forms)

Synonymy

Antonymy

2. Semantic Relations (between word meanings)

Hyponymy/Hypernymy

Meronymy/Holonymy

Entailment/Troponymy

hindi wordnet apis
findtheinfo getindex

in_wn index_lookup read_synset

free_synset

free_index morphstr

Hindi Data

Hindi WordNet APIs
approach to wsd
Approach to WSD ….

Hindi Wordnet

Hindi Document

Intersection

Similarity

Context Bag Semantic Bag

wsd algorithm
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
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
Evaluation
  • Only Nouns
  • Test corpora from CIIL, Mysore.
  • Corpus from 8 domains, each containing around 2000 words on an average.
conclusions knowledge based mt
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
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
Pre Editing

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

The inspection team {which was} appointed by the United Nations visited Iraq {in} early July, 2003.

post editing
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
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

Available

    • He bought a pen
    • ]snanao ek klama KrIda
    • All ministers have huge houses
    • saBaI pMtaoMko pasa bahut baDo Gar hOM

New

    • He bought a huge house
    • ]snanao ek bahut baDa Gar KrIda
pitfall of translation memory
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.

back

co reference resolution
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.
elipsis
Elipsis

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.

back

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