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Inter-set SMT - MOSES og POS. Lene Offersgaard, Claus Povlsen Center for Sprogteknologi SDMT-SMV2 workshop 25. september 2007. Translation Workflow. English text. SMT : Statistical resources. Preprocessing. Translation Engine. Language model srilm 3. MOSES Decoder. Phrase table.

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inter set smt moses og pos
Center for Sprogteknologi

Inter-set SMT - MOSES og POS

Lene Offersgaard, Claus Povlsen

Center for Sprogteknologi

SDMT-SMV2 workshop 25. september 2007

translation workflow
Center for SprogteknologiTranslation Workflow

English

text

  • SMT:
  • Statistical
  • resources

Preprocessing

Translation Engine

Language model

srilm 3

MOSES

Decoder

Phrase table

Postprocessing

Danish text

Proff reading

adding linguistic information to smt moses
Center for SprogteknologiAdding linguistic information to SMT: MOSES
  • MOSES
  • Open source system replacing Pharaoh (Koehn et al. 2007)
  • State-of-the-art phrase-based approach
  • Using factored translation models
  • Comparison Pharao and Moses decoder
  • Reuse of statistical resources possible
adding linguistic information using moses
Center for SprogteknologiAdding linguistic information using MOSES
  • Using factored translation models
  • Makes it possible to build translation models based on surface forms, part-of-speech, morphology etc.
  • We use:
  • Translation model: word->word, pos->pos
  • Generation model determine the output

Input

Output

word

word

pos+morf

pos+morf

results adding pos tags by inspection
Center for SprogteknologiResults adding pos-tags – by inspection
  • With inclusion of morpho-syntactic information:
  • (lit:… control of the full spectrum)
  • ... kontrol af det fulde spektrum (gender agreement)
  • (lit: … the active ingredients)
  • ... de aktive bestanddele (number agreement)
  • (lit: ... this constant erosion)
  • ... denne konstante erosion (definiteness agreement)
adding linguistic information using moses1
Center for SprogteknologiAdding linguistic information using MOSES
  • Using factored translation models
  • Makes it possible to build translation models based on surface forms, part-of-speech, morphology etc.
  • We use:
  • Translation model: word->word, pos->pos
  • Generation model determine the output

Input

Output

word

word

pos+morf

pos+morf