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Approaching a New Language in Machine Translation

Approaching a New Language in Machine Translation. Anna Sågvall Hein, Per Weijnitz. A Swedish example. Experiences of rule-based translation by means of translation software that was developed from scratch statistical translation by means of publicly available software.

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Approaching a New Language in Machine Translation

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  1. Approaching a New Language in Machine Translation Anna Sågvall Hein, Per Weijnitz

  2. A Swedish example • Experiences of • rule-based translation by means of translation software that was developed from scratch • statistical translation by means of publicly available software SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  3. Developing a robust transfer-based system for Swedish • collecting a small sv-en translation corpus from the automotive domain (Scania) • building a prototype of a core translation engine, Multra • extending the translation corpus to 50k words for each language and scaling-up the dictionaries for the extended corpus • building a translation system, Mats for hosting Multra and processing real-word documents • making the system robust, transparent and trace-able • building an extended, more flexible version of Mats, Convertus SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  4. Features of the Multra engine • transfer-based • modular • analysis by chart parsing • transfer based on unification • generation based on unification and concatenation • non-deterministic processing • preference machinery SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  5. Features of the host system(s) • robust • always produces a translation • modular • a separate module for each translation step • transparent • text based communication between modules • trace-able • step-wise for each module • evaluation of the linguistic coverage • counting and collecting missing units from each module • process communication • MATS, unidirectional pipe • Convertus, blackboard SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  6. Robustness • dictionary • complementary access to external dictionaries • analysis • exploiting partial analyses • concatenation of sub-strings in preserved order • transfer • only differences covered by rules • generation • token translations presented in source language order • fall back generations cleaned up using a language model SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  7. Language resources, full system • analysis • dictionary • grammar • transfer • dictionary • grammar • generation • dictionary • grammar • external translation dictionary • target language model SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  8. Language resources, simplified, direct translation system • analysis • dictionary • transfer • dictionary • generation • dictionary • external translation dictionary • target language model SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  9. Achievements • Bleu scores ~0.4-0.5 for training materials • automotive service literature • EU agricultural texts • security police communication • academic curricula SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  10. Current project • Translation of curricula of Uppsala University from Swedish to English SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  11. Current development • initial studies of automatic extraction of grammar rules from text and tree-banks for parsing and generation • inspired by • Megyesi, B. (2002). Data-Driven Syntactic Analysis - Methods and Applications for Swedish. Ph.D.Thesis. Department of Speech, Music and Hearing, KTH, Stockholm, Sweden. • Nivre, J., Hall, J. and Nilsson, J. (2006) MaltParser: A Data-Driven Parser-Generator for Dependency Parsing. In Proceedings of LREC. SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  12. Statistical MT • Publicly available software: • decoder • Pharaoh (Koehn 2004) • translation models • UPlug (Tiedemann, J. 2003) • GIZA++ (Och, F. J. and Ney, H. 2000) • Thot (Ortiz-Martínez, D. et al. 2005) • language models • SRILM (Stolcke, A. 2002) SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  13. Success factors • language differences • translation direction • size of training corpus • density of corpus • corpus density: lexical openness, degree of repetetiveness of n-grams, plus other significant factors • How can they be appropriately formalised? Measured? Combined? SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  14. Experiments • limited amount of training data (assumed for minority languages) <=32k sentence pairs • Swedish represents the minority lang. • search for correlation between density of corpus and translation quality SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  15. Mats automotive corpus SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  16. Europarl SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  17. Mats & Europarl, density in terms of type/occurrence ratio SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  18. BLEU for Europarl: 10 SL->sv SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  19. BLEU for Europarl: sv->10 TL SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  20. 4-gram type/occurrence ratio, SL->sv SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  21. 3-gram type/occurrence ratio, SL->sv SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  22. Detailed view, Europarl, sv->en • Examining the correlation between SL n-gram type/occurrence – density - and BLEU. SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  23. Detailed view, Europarl sv-fi • Examining the correlation between SL n-gram type/occurrence – density - and BLEU. SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  24. Rule-based and statistical - moving slightlyoff domain • MATS automotive corpus used for training, 16k • test data from Mats (outside training data) and from separate, similar corpus: Scania98 SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  25. Correlation between overlap and performance - Pharaoh • MATS automotive corpus used for training, 16k • test data from MATS and Scania98 • measured occurrences of test data units that also occur in the training data • test and training source language data overlap: the precondition for successful data driven MT SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  26. Summary • development of Convertus, a robust transfer-based system equipped with language resources for sv-en translation in several domains • BLEU measures of smt using publicly available software (Pharaoh) and Europarl • 10 languages, two translation directions, and training intervals of 5k sentence pairs up to 32k • data on density of Europarl in terms of overlaps • comparing rbmt and smt using Convertus and Pharaoh • searching for a formal way of quantifying how well a corpus will work for SMT • starting with density of source language SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  27. Concluding remarks • building a rule-based system from scratch is a major undertaking • customizing existing software is better • smt systems can be built fairly easily using publicly available software • restrictions on commercial use, though • factors influencing quality in smt • size of training corpus • density of source side of training corpus • language differences and translation direction • other important factors (future work) • quality of training corpus, alignment quality, … SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

  28. Concluding remarks (cont.) • smt versus rbmt • smt seems more sensitive to density than rbmt • error analysis and correction can be linguistically controlled in rbmt as opposed to smt SALTMIL, LREC 2006, Sågvall Hein & Weijnitz

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