© Guy De Pauw, Naomi Maajabu, Peter Waiganjo Wagacha

1. A Knowledge-Light Approach to Luo Machine Translation and Part-of-Speech Tagging Guy De Pauw(guy@aflat.org) Naomi Maajabu (naomi@aflat.org) Peter WaiganjoWagacha (waiganjo@aflat.org)

2. Outline Resource-scarce language engineering The case of Luo (Dholuo) A trilingual parallel corpus English – Swahili – Luo Machine Translation experiments Projection of Annotation experiments Conclusion & Future Work

3. Resource-Scarce Languages Limited financial, political, … resources Few digital resources: digital lexicons, corpora Bottleneck of linguistic expertise (in LT) Two approaches: Rule-based approaches Advantages: meticulous design, linguistically relevant Corpus-based, data-driven approaches Growing importance and availability of digital text material Advantages: performance models, fast development, automatic quantitative evaluation

4. Dholuo UG KE DRC RW BU Western Nilotic language Spoken by +3M Luo people Kenya, Uganda, Tanzania No official dialect Tonal, but not marked in orthography Latin alphabet, no diacritics Resource-scarce (not official language) Web-mined corpus of 200k words De Pauw, Wagacha & Abade (2007) Unsupervised Induction of Dholuo Word Classes using Maximum Entropy Learning TZ

5. Most famous Luo Nilotic language Spoken by +3M people in Kenya, Tanzania and Uganda Not an official language Latin script Most famous Luo:

6. AfLaT 2009 / LRE (submitted) SAWA CORPUS 2 million word parallel corpus English – Swahili Competitive machine translation results Projection of annotation of part-of-speech tags from English into Swahili is viable But what about true resource-scarce languages?

7. Parallel Data for Luo International Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luo Use English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpus Preprocessing: Pdftext conversion Tokenization Sentence alignment

8. Parallel Data for Luo International Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luo Use English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpus Preprocessing: Pdftext conversion Koolwire.com Tokenization Sentence alignment

9. Parallel Data for Luo International Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luo Use English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpus Preprocessing: Pdftext conversion Koolwire.com Tokenization Sentence alignment Luk 1:1 Jimang'enyosebedo ka chanoweche mane otimore e dierwa , Luk 1:2 Mana kaka nochiwgi ne wan kodjogomotelo mane jonenowang'giwang kendo jotichwach . Luk 1:3 Kuommano , an bende kaka asenonotiendwechegimalong'onyaka a chakruok , en gimaberbendemondoandikni e yomochanoremaler , in mulourTheofilo . Luk 1:4 Mondoing'eadier mar gikmosepwonji . Luk 1:5 E ndalo ma Heroderuodh Judea , ne nitiejadolo ma nyingeZakaria , ma ne en achielkuomogandajodolomagAbija ; chiege Elizabeth bende ne nyardhoodHarun . Luk 1:6 Gidutojariyo ne gin jomakarenyimNyasaye , ne giritochikemadongogimatindomagRuothNyasaye , maongeketho . Luk 1:7 To ne giongeginyithindo , nikech Elizabeth ne migumba , kendo gin jariyogohikgi : nose niang' Luk 1:8 Chieng' morokaneogandagiZakaria ne ni e tich to notiyo kaka jadolo e nyimNyasaye , Luk 1:9 Noyieregiombulu kaka chik mar jodolo , mondoodonjieihekalu mar Ruoth kendo owangubani . Luk 1:10 To ka sa mar wang'oubaniochopo , jolemodutonochokoreoko kendo negilamo . Luk 1:11 Ekamalaika mar RuothNyasayenofwenyorene , kochungo bath kendo mar ubanikorachwich .

10. Parallel Data for Luo International Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luo Use English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpus Preprocessing: Pdftext conversion Koolwire.com Tokenization Sentence alignment R.C.Moore (2002) Fast and accurate sentence alignment of bilingual corpora.

12. 10% validation set

13. 10% test set (partly annotated for pos-tags)Tiny, register-specific parallel corpus

15. Misconception:morphologically rich languages cannot be used in statistical machine translation, since word-alignment is word-basedhave turned him down I nimemkatalia

17. Misconception: morphologically rich languages cannot be used in statistical machine translation, since it is word-based

18. Word alignment and language modeling can be enhanced by using factored data

20. Factored Data

21. Machine Translation Experiments English  Luo and Swahili  Luo Use standard SMT tool MOSES (Koehn et al 2007) Phrase-based machine translation Can handle factored data Uses SRILM language modeling tool (Stolcke 2002) English: Gigaword corpus Swahili: TshwaneDJe Kiswahili Internet Corpus Luo: 200k Luo corpus + Training/Evaluation Set of New Testament data

22. Results OOV: percentage of out-of-vocabulary words (i.e. words unknown to the language model) BLEU: Bilingual Evaluation Understudy (calculates n-gram overlap between reference and machine translation) NIST: modification of BLEU, taking into account information value of n-grams BLEU & NIST attempt to optimize correlation with human evaluation

24. SMT Experiments

25. Examples

27. Project part-of-speech tags from resource-rich(er) language

28. Direct correspondence assumption (Hwa 2002)

29. Tag-projection list + Tag priority list

31. Errors made mainly on function words (closed class)

32. Possible improvements:

33. Translation dictionary

35. Conclusion First proof-of-the-principle experiments (machine translation, projection of annotation) for a Nilotic language “If you have a digital bible, you have an MT system and other NLP components” Small register-specific parallel corpus English – Swahili – Luo Modest, but encouraging BLEU & NIST scores SMT for Luo is possible No alternatives (cf. other African languages?) Factored data can overcome limitations of pure word-based methods for word-alignment. Morphological generation on the target language side is still a bottleneck

36. Future Work Make trilingual, annotated corpus available through Open-Content Text Corpus (SAWA corpus will be made available soon as well) Use translation model as seed for bilingual web mining Tweak & tune MOSES parameters to improve quality Better morphological analysis, generation for Dholuo Unsupervised morphology induction Use automatically induced annotation as training data for supervised data-driven taggers Repeat experiment for other resource-scarce languages

37. Demonstration System http://aflat.org/luomt .org