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Machine Translation Distortion Model

Machine Translation Distortion Model. Stephan Vogel Spring Semester 2011. Recap: DM in Word Alignment Models. HMM alignment: Jump model Can be conditioned on word classes Balance between data and parameters in model Larger corpora -> richer models. F. 3. 0. -1. 2. E. Distance Model.

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Machine Translation Distortion Model

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  1. Machine TranslationDistortion Model Stephan Vogel Spring Semester 2011 Stephan Vogel - Machine Translation

  2. Recap: DM in Word Alignment Models • HMM alignment: Jump model • Can be conditioned on word classes • Balance between data andparameters in model • Larger corpora -> richer models F 3 0 -1 2 E Stephan Vogel - Machine Translation

  3. Distance Model • Decoder typically generates target sequence sequentially, while jumping forth and back on source sentence • Simplest reordering model • Cost of a reordering depends only on the distance of the reordering • Distribution can be estimated from alignment • Or just a Gaussian with mean 1 • Or log p( aj | aj-1, I) = aj – aj-1 i.e. reordering cost proportional to distance Stephan Vogel - Machine Translation

  4. Lexicalized Reordering Models • Instead of conditioning on classes, condition on actual words • Different possibilities: • Condition on source words vs target words • Condition on words at start of jump (out-bound) vs words at landing point (in-bound) F F E E Stephan Vogel - Machine Translation

  5. Block Distortion Model • Given current block, look at links at the corners • Top: how did I come from previous phrase? • Bottom: how do I continue to next phrase? F Previous Block Left Top Right Top Current Block Current Block Next Block Left Bottom Right Bottom E Stephan Vogel - Machine Translation

  6. Block Distortion Model • Top-Left: prev-to-current = monotone F Previous Block Left Top Current Block E Stephan Vogel - Machine Translation

  7. Block Distortion Model • Top-Right: prev-to-current = swap F Previous Block Right Top Current Block E Stephan Vogel - Machine Translation

  8. Block Distortion Model • Neither top-left nor top-right: prev-to-current = disjoint F Previous Block Current Block E Stephan Vogel - Machine Translation

  9. Block Distortion Model • Bottom-Right: current-to-next = monotone F Current Block Next Block E Stephan Vogel - Machine Translation

  10. Block Distortion Model • Bottom-Left: current-to-next = swap F Current Block Next Block E Stephan Vogel - Machine Translation

  11. Block Distortion Model • Neither bottom-Left nor bottom-right: current-to-next = disjoint F Current Block Next Block E Stephan Vogel - Machine Translation

  12. Moses Code // orientation to previous E bool connectedLeftTop = isAligned( sentence, startF-1, startE-1 ); bool connectedRightTop = isAligned( sentence, endF+1, startE-1 ); if ( connectedLeftTop && !connectedRightTop) extractFileOrientation << "mono"; else if (!connectedLeftTop && connectedRightTop) extractFileOrientation << "swap"; else extractFileOrientation << "other"; // orientation to following E bool connectedLeftBottom = isAligned( sentence, startF-1, endE+1 ); bool connectedRightBottom = isAligned( sentence, endF+1, endE+1 ); if ( connectedLeftBottom && !connectedRightBottom) extractFileOrientation << " swap"; else if (!connectedLeftBottom && connectedRightBottom) extractFileOrientation << " mono"; else extractFileOrientation << " other"; Stephan Vogel - Machine Translation

  13. Block Distortion Model • For each phrase pair 6 counts: 2 groups of 3 • From previous: monotone swap other • To next: monotone swap other • Normalize for each group • We do not model p( orientation | phase_pair_1, phrase_pair_2 ) • Many overlapping and embedded blocks • Would be too sparse • We model p( orientation | phrase_pair, entering )and p( orientation | phrase_pair, leaving ) • I.e. not really looking at the previous block, but only at the alignment link • For each entry in the phrase table we have an entry in the distortion model Stephan Vogel - Machine Translation

  14. Distortion Model Table acuerdo con el lugar de ||| according to the place of ||| 0.14286 0.14286 0.71429 0.71429 0.14286 0.14286 acuerdo con nuestra información ||| according to our information ||| 0.14286 0.14286 0.71429 0.71429 0.14286 0.14286 acuerdo de pesca con Marruecos ||| fisheries agreement with Morocco ||| 0.92982 0.01754 0.05263 0.78947 0.01754 0.19298 acuerdo entre Israel y ||| agreement ||| 0.20000 0.20000 0.60000 0.20000 0.20000 0.60000 acuerdo no porque sea bueno , ||| agreement not because it is good , ||| 0.60000 0.20000 0.20000 0.60000 0.20000 0.20000 acuerdo sobre este punto ||| agreed on ||| 0.20000 0.20000 0.60000 0.20000 0.20000 0.60000 acuerdos a largo plazo se iniciaron en ||| long-term arrangements began in ||| 0.60000 0.20000 0.20000 0.60000 0.20000 0.20000 acuerdos globales , especialmente ||| global agreements - primarily |||0.20000 0.20000 0.60000 0.60000 0.20000 0.20000 • Many entries 0.6 0.2 … • Phrase pair seen only once • Simple smoothing Stephan Vogel - Machine Translation

  15. Distance-based ITG Reordering Model • Simple ITG model had very weak reordering model • Condition it on size of blocks (subtrees) • Condition on distance (e.g. taken from HMM alignment) F E Stephan Vogel - Machine Translation

  16. Summary • Distortion models in word alignment models • Decoders work on phrases -> distortion models or phrases • In Moses: Block reordering (also called lexicalized) • Conditioned on phrase pair • Monotone, swap, disjoint • Alternatives • Based on words at the boundaries • Inbound/Outbound • Easy to have lexicalized distortion model for ITG Stephan Vogel - Machine Translation

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