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Automatic Translation of Nominal Compound into Hindi

Automatic Translation of Nominal Compound into Hindi

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Automatic Translation of Nominal Compound into Hindi

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  1. Automatic Translation of Nominal Compound into Hindi Prashant Mathur IIIT Hyderabad Soma Paul IIIT Hyderabad

  2. OUTLINE • What is a Nominal Compound (NC) ? • Translation variation of English NC into Hindi • Motivation • Approach • Results • Future Work • Bibliography Prashant Mathur

  3. Nominal Compound • A construct of two or more nouns. • The rightmost noun being the head, preceding nouns modifiers. Oil Pump: a device used to pump oil Customer satisfaction indices : index that indicates the satisfaction rate of customer • Two word nominal compounds are the object of study here Prashant Mathur

  4. Frequency of NC in English Corpus (Baldwin et al 2004) Prashant Mathur

  5. OUTLINE • What is a Nominal Compound (NC) ? • Translation variation of English NC into Hindi • Motivation • Approach • Results • Future Work • Bibliography Prashant Mathur

  6. Variation in translating English NC into Hindi As Nominal Compound • ‘Hindu texts’ hindU SastroM, ‘milk production’ dugdha utpAdana As Genitive Construction • ‘rice husk’ cAval kI bhUsI, • ‘room temperature’ kamare ka tApamAna As one word • Cow dung gobar As Adjective Noun Construction • ‘nature cure’ prAkratik cikitsA, ‘hill camel’ ‘pahARI UMTa’ As other syntactic phrase • wax work mom par kalAkArI ‘work on wax’, • body pain SarIr meM dard ‘pain in body’ Others • Hand luggage haat meM le jaaye jaane vaale saamaan Prashant Mathur

  7. OUTLINE • What is a Nominal Compound (NC) ? • Translation variation of English NC into Hindi • Motivation • Approach • Results • Future Work • Bibliography Prashant Mathur

  8. Motivation • Issues in translation • Choice of the appropriate target lexeme during lexical substitution; and • Selection of the right target construct type. • Occurrence of NCs in a corpus is high in frequency, however individual compound occur only a few times. • NCs are too varied to be precompiled in an exhaustive list of translated candidates Prashant Mathur

  9. Therefore … • NCs are to be handled on the fly. • The task of translation of NCs from English into Hindi becomes a challenging task of NLP Prashant Mathur

  10. With Google translator • When tested on the same dataset that has been used to evaluate our system Prashant Mathur

  11. OUTLINE • What is a Nominal Compound (NC) ? • Translation variation of English NC into Hindi • Motivation • Approach • Results • Future Work • Bibliography Prashant Mathur

  12. Approach • Translation template generation • Extraction of NC from English corpus • Sense disambiguation of components • Lexical substitution of the component nouns using Bi-Lingual Dictionary • Preparing translation candidates • Corpus Search of translation candidates and their Ranking. Prashant Mathur

  13. Translation Template Generation We did the survey of 50,000 sentences of parallel corpora and found out the following construction types. Prashant Mathur

  14. Some Templates Total of 44 templates were formed, some of them are showed below. • Nominal Compound • H1 H2 • Genitive • H1 kA H2 • H1 ke H2 • H1 kI H2 • Long Phrases • H1 pe H2 • H1 meM H2 • H1 par H2 • H1 ke xvArA H2 • H1 se prApwa H2 • Adjective • H1-ikA H2 • Single-Word • H1 Prashant Mathur

  15. Approach • Translation template generation • Extraction of NC from English corpus • Sense disambiguation of components • Lexical substitution of the component nouns using Bi-Lingual Dictionary • Preparing translation candidates • Corpus Search of translation candidates and their Ranking. Prashant Mathur

  16. Extraction 1Tree-Tagger is a POS-Tagger which gives some extra information. Word  Tree-Tagger  word POS TAG lemma rods  rods_NNS_rod 2As assumed previously we consider only Noun-Noun formation as Nominal Compound. Prashant Mathur

  17. Approach • Translation template generation • Extraction of NC from English corpus • Sense disambiguation of components • Lexical substitution of the component nouns using Bi-Lingual Dictionary • Preparing translation candidates • Corpus Search of translation candidates and their Ranking. Prashant Mathur

  18. Lexical Substitution Prashant Mathur

  19. Step 3 : Sense Disambiguation of components • To reduce the number of translation candidates • Example : Campaigns for road safety are organized to keep everyone safer on the Indian roads Prashant Mathur

  20. WordNet Sense-Relate by Ted Peterson. • 80% accuracy in case of NC disambiguation. Prashant Mathur

  21. Approach • Translation template generation • Extraction of NC from English corpus • Sense disambiguation of components • Lexical substitution • Preparing translation candidates • Corpus Search of translation candidates and their Ranking. Prashant Mathur

  22. Lexical Substitution • Now how to translate it into Hindi ? • We don’t have direct wordnet mapping from English to Hindi. • We use alternative method to translate. Prashant Mathur

  23. Step 4: Lexical Substitution • Acquire all possible translations for all the words within a synset. Prashant Mathur

  24. Contd… • Select those Hindi words which are common translations to all English words of a synset, if there is one Selected words are: maarg, saDak, raastaa All words are selected Prashant Mathur

  25. Approach • Translation template generation • Extraction of NC from English corpus • Sense disambiguation of components • Lexical substitution • Preparing translation candidates • Corpus Search of translation candidates and their Ranking. Prashant Mathur

  26. Step 5: Preparing Translation Candidate • For “road safety” • Templates generated are: mArga para surakRA, mArga surakRA, SaDak para surakRA, SaDak kI surakRA ... Prashant Mathur

  27. Approach • Translation template generation • Extraction of NC from English corpus • Sense disambiguation of components • Lexical substitution • Preparing translation candidates • Corpus Search of translation candidates and their Ranking. Prashant Mathur

  28. Step 6 Corpus Search • Hindi Corpus (Raw): 28 million words • Indexed • Search – pattern match Prashant Mathur

  29. Example • election time  cunAva ke samaya • temple community  maMxira kA samAja • marriage customs  vivAha kI praWA … But we didn’t found any translation for road safety  Ф Prashant Mathur

  30. CTQ (Corpus based Translation Quality) • Rate a given translation candidate for both • The fully specified translation and • Its parts in the context of the translation template in question. CTQ (w1H , w2H , t) = αP(w1H , w2H , t) +βP(w1H,t) P(w2H , t) P(t) • t is the translation template used • w1H, w2H are the translations of components of NC • α = 1, β=0 if P(w1H , w2H , t) > 0 (didn’t perform variation in α,β constants) Prashant Mathur

  31. Contd.. • Example • road safety P(w1H , w2H , t) = 0 • road  mArga, mArgake, mArgameM, saDaka, saDaka par … • safety  surakRA, kesurakRA, meMsurakRA, … so on • P (mArga, meM) * P(meM, surakRA) * P(meM) = (2.28*10-5) * (9.14*10-6) * (.286) = 6 * 10-11 • P (mArga, kI) * P(kI, surakRA) * P(kI) = (1.35 × 10-5) * (3.82857143 × 10-5) * (.228) = 1.17 × 10-10 • Higher probablity for “mArgakIsurakRA” Prashant Mathur

  32. Ranking • Baseline Ranking: • Count based ranking • A stronger ranking measure CTQ (borrowed from Baldwin and Tanaka (2004)) Prashant Mathur

  33. Results 62.1 56.2 54.1 53.6 50 46.1 28 28.5 24.6 19 24 14 Prashant Mathur

  34. Contd.. • Measure taken to improve recall: • By using genitives as default construct when translation for a NC is not found • Motivation: • We conduct one experiment on development data • We verify whether the NCs for which no translation found during corpus search can be legitimately translated as a genitive construct • We found the heuristics is working for 59% cases Prashant Mathur

  35. Results • Using genitive as default construct where the system fails to produce a translation 57 54 44.5 24.8 Prashant Mathur

  36. Related works • Similar approaches (search of translation templates in the corpus) adopted in • Bungum and Oepen (2009) for Norwegian to English nominal compound translation • Tanaka and Baldwin (2004) for English to Japanese nominal compound and vice versa Prashant Mathur

  37. Conclusion • Novelty of our approach • Using a WSD tool on Source language - to select the correct sense of nominal components • The result : The number of possible translation candidates to be searched in the target language corpus is significantly reduced. Prashant Mathur

  38. Future Work • Multinary NC translation • Using semantic features provided in UW-Dictionary • Varying α & β in ranking technique to produce more effective results. Prashant Mathur

  39. Bibliography • Translation by Machine of Complex Nominals: Getting it right • Tanaka and Timothy Baldwin • Translation Selection for Japanese-English Noun-Noun Compounds • Tanaka, Takaaki and Timothy Baldwin • Automatic Translation Of Noun Compounds • Rackow, Ido Dagan, Ulrike Schwall • Norwegian to English nominal compound translation • Bungum, Oepen Prashant Mathur