This document discusses experiments in machine translation and part-of-speech tagging for the Luo language using a small parallel corpus containing English, Swahili, and Luo text from the Bible. Machine translation systems were built using the Moses toolkit that achieved modest BLEU and NIST scores. Part-of-speech tags were projected from English and Swahili onto Luo text using word alignments, with errors mainly on function words. The results provide a starting point for natural language processing in Luo despite it being a resource-scarce language.

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

2. OutlineResource-scarce language engineeringThe case of Luo (Dholuo)A trilingual parallel corpus English – Swahili – LuoMachine Translation experimentsProjection of Annotation experimentsConclusion & Future Work

3. Resource-Scarce LanguagesLimited financial, political, … resourcesFew digital resources: digital lexicons, corporaBottleneck of linguistic expertise (in LT)Two approaches:Rule-based approachesAdvantages: meticulous design, linguistically relevantCorpus-based, data-driven approachesGrowing importance and availability of digital text materialAdvantages: performance models, fast development, automatic quantitative evaluation

4. DholuoUGKEDRCRWBUWestern Nilotic languageSpoken by +3M Luo peopleKenya, Uganda, TanzaniaNo official dialectTonal, but not marked in orthographyLatin alphabet, no diacriticsResource-scarce (not official language)Web-mined corpus of 200k wordsDe Pauw, Wagacha & Abade (2007) Unsupervised Induction of Dholuo Word Classes using Maximum Entropy LearningTZ

5. Most famous LuoNilotic languageSpoken by +3M people in Kenya, Tanzania and Uganda Not an official languageLatin scriptMost famous Luo:

6. AfLaT 2009 / LRE (submitted)SAWA CORPUS2 million word parallel corpus English – SwahiliCompetitive machine translation resultsProjection of annotation of part-of-speech tags from English into Swahili is viableBut what about true resource-scarce languages?

7. Parallel Data for LuoInternational Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luoUse English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpusPreprocessing:Pdftext conversionTokenizationSentence alignment

8. Parallel Data for LuoInternational Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luoUse English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpusPreprocessing:Pdftext conversionKoolwire.comTokenizationSentence alignment

9. Parallel Data for LuoInternational Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luoUse English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpusPreprocessing:Pdftext conversionKoolwire.comTokenizationSentence alignmentLuk 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 LuoInternational Bible Society (2005) Luo New Testament. Available at http://www.biblica.com/bibles/luoUse English and Swahili New Testament data of SAWA corpus to construct small trilingual parallel corpusPreprocessing:Pdftext conversionKoolwire.comTokenizationSentence alignmentR.C.Moore (2002) Fast and accurate sentence alignment of bilingual corpora.

11. Trilingual corpus80% training set

12. 10% validation set

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

14. Word alingmentGoal: word aligned corpus

15. Misconception:morphologically rich languages cannot be used in statistical machine translation, since word-alignment is word-basedhaveturnedhimdownInimemkatalia

16. Factored DataGoal: word aligned corpus

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

19. General idea: use extra annotation layers (part-of-speech tagging, lemmatization) to aid discovery of possible translation pairsFactored Data

20. Factored Data

21. Machine Translation ExperimentsEnglish  Luo and Swahili  LuoUse standard SMT tool MOSES (Koehn et al 2007)Phrase-based machine translationCan handle factored dataUses SRILM language modeling tool (Stolcke 2002)English: Gigaword corpusSwahili: TshwaneDJe Kiswahili Internet CorpusLuo: 200k Luo corpus + Training/Evaluation Set of New Testament data

22. ResultsOOV: 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-gramsBLEU & NIST attempt to optimize correlation with human evaluation

23. SMT ExperimentsTranslation dictionary did not significantly improve results, but factored data didSMT Experiments

24. SMT Experiments

25. Examples

26. Projection of annotationUse word alignment to bootstrap annotation in a resource-scarce language

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