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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. 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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  1. Machine Translation, Language Divergence and Lexical Resources Pushpak Bhattacharyya Computer Science and Engineering Department IIT Bombay

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

  3. What is MT Conversion of source language text to target language text Computer Program Document in L2 Document in L1

  4. 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

  5. Kinds of MT Systems(domain coverage) • General Purpose (SYSTRAN in Europe) • Domain Specific (Tom-Mateo in Canada; Translates weather reports between French and English)

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

  7. Why is MT difficult?Classical NLP problems • Ambiguity • Lexical • Structural • Ellipsis • Co-reference • Anaphora • Hypernymic examples

  8. Why is MT DifficultLanguage Divergence • Lexico-Semantic Divergence • Structural Divergence

  9. 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।

  10. 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।

  11. 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।

  12. 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।

  13. Language Divergence(English Hindi: Preposition to Adverb) • It gets cooler toward evening. (near a point in time) • शाम होते-होते ठंडक बढ़ जाती है।

  14. 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।

  15. 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.

  16. 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,...

  17. 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.

  18. Language Divergence Theory: Lexico-Semantic Divergences • Conflational divergence • Structural divergence • Categorial divergence • Head swapping divergence • Lexical divergence

  19. Language Divergence Theory: Syntactic Divergences • Constituent Order divergence • Adjunction Divergence • Preposition-Stranding divergence • Null Subject Divergence • Pleonastic Divergence

  20. MT approaches interlingua Based Direct Transfer Based Vaquiouse Triangle

  21. 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

  22. Competing approaches Direct Transfer based

  23. 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

  24. Transfer Based Source sentence processed for parsing, chunking etc. S VP NP V NP I like mangoes

  25. Transfer Based Transfer structures obtained for the target sentence. S VP NP NP V I mangoes like

  26. Transfer Based Morphology and language specific modifications S VP NP NP V mauJao AcCa lagataa hO Aama

  27. 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

  28. 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.

  29. 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)

  30. Semantics: use of Lexical Resources • WordNet • Word Sense Disambiguation

  31. 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.

  32. Lexical Matrix

  33. 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

  34. A small part of Hindi Wordnet

  35. findtheinfo getindex in_wn index_lookup read_synset free_synset free_index morphstr Hindi Data Hindi WordNet APIs

  36. The Hindi WSD System

  37. Approach to WSD …. Hindi Wordnet Hindi Document Intersection Similarity Context Bag Semantic Bag

  38. 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

  39. 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

  40. Evaluation • Only Nouns • Test corpora from CIIL, Mysore. • Corpus from 8 domains, each containing around 2000 words on an average.

  41. Result

  42. 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

  43. 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))

  44. 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>.

  45. Post Editing • back (I want to eat well today) MMmaOM Aaja AcCa Kanaa caahta hUM mauJao Aaja AcCa Kanaa caaihe

  46. 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

  47. 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

  48. Ambiguity Chair

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