1 / 27

MEMT: Multi-Engine Machine Translation

MEMT: Multi-Engine Machine Translation. Faculty: Alon Lavie, Robert Frederking, Ralf Brown, Jaime Carbonell Students: Shyamsundar Jayaraman, Satanjeev Banerjee. Goals and Approach.

watsonthomas
Télécharger la présentation

MEMT: Multi-Engine Machine Translation

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. MEMT:Multi-Engine Machine Translation Faculty: Alon Lavie, Robert Frederking, Ralf Brown, Jaime Carbonell Students: Shyamsundar Jayaraman, Satanjeev Banerjee

  2. Goals and Approach • Combine the output of multiple MT engines into a synthetic output that outperforms the originals in translation quality • Synthetic combination of the originals, NOT selecting the best system • Experimented with two approaches: • Approach-1: Merging of Lattice outputs + joint decoding • Each MT system produces a lattice of translation fragments, indexed based on source word positions • Lattices are merged into a single common lattice • Statistical MT decoder selects a translation “path” through the lattice • Approach-2: Align best output from engines + new decoder • Each MT system produces a sentence translation output • Establish an explicit word matching between all words of the various MT engine outputs • “Decoding”: create a collection of synthetic combinations of the original strings based on matched words, target LM, and constraints + re-combination and pruning • Score resulting hypotheses and select a final output MEMT

  3. Approach-2: Sentence MEMT • Idea: • Start with output sentences of the various MT engines • Explicitly align the words that are common between any pair of systems, and apply transitivity • Use the alignments as reinforcement and as indicators of possible locations for the words • Each engine has a “weight” that is used for the words that it contributes • Decoder searches for an optimal synthetic combination of words and phrases that optimizes a scoring function that combines the alignment weights and a LM score MEMT

  4. The Sentence Matcher • Developed by Satanjeev Banerjee as a component in our METEOR Automatic MT Evaluation metric • Finds maximal alignment match with minimal “crossing branches” • Implementation: Clever search algorithm for best match using pruning of sub-optimal sub-solutions MEMT

  5. Matcher Example IBM: the sri lankan prime minister criticizes head of the country's ISI: The President of the Sri Lankan Prime Minister Criticized the President of the Country CMU: Lankan Prime Minister criticizes her country MEMT

  6. The MEMT Algorithm • Algorithm builds collections of partial hypotheses of increasing length • Partial hypotheses are extended by selecting the “next available” word from one of the original systems • Sentences are assumed synchronous: • Each word is either aligned with another word or is an alternative of another word • Extending a partial hypothesis with a word “pulls” and “uses” its aligned words with it, and marks its alternatives as “used” – “vectors” keep track of this • Partial hypotheses are scored and ranked • Pruning and re-combination • Hypothesis can end if any original system proposes an end of sentence as next word MEMT

  7. The MEMT Algorithm • Scoring: • Alignment score based on reinforcement from alignments of the words • LM score based on trigram LM • Sum logs of alignment score and LM score (equivalent to product of probabilities) • Select best scoring hypothesis based on: • Total score (bias towards shorter hypotheses) • Average score per word MEMT

  8. The MEMT Algorithm • Parameters: • “lingering word” horizon: how long is a word allowed to linger when words following it have already been used? • “lookahead” horizon: how far ahead can we look for an alternative for a word that is not aligned? • “POS matching”: limit search for an alternative to only words of the same POS MEMT

  9. Example IBM: korea stands ready to allow visits to verify that it does not manufacture nuclear weapons 0.7407 ISI: North Korea Is Prepared to Allow Washington to Verify that It Does Not Make Nuclear Weapons 0.8007 CMU: North Korea prepared to allow Washington to the verification of that is to manufacture nuclear weapons 0.7668 Selected MEMT Sentence : north korea is prepared to allow washington to verify that it does not manufacture nuclear weapons . 0.8894 (-2.75135) MEMT

  10. Example IBM: victims russians are one man and his wife and abusing their eight year old daughter plus a ( 11 and 7 years ) man and his wife and driver , egyptian nationality . : 0.6327 ISI: The victims were Russian man and his wife, daughter of the most from the age of eight years in addition to the young girls ) 11 7 years ( and a man and his wife and the bus driver Egyptian nationality. : 0.7054 CMU: the victims Cruz man who wife and daughter both critical of the eight years old addition to two Orient ( 11 ) 7 years ) woman , wife of bus drivers Egyptian nationality . : 0.5293 MEMT Sentence : Selected : the victims were russian man and his wife and daughter of the eight years from the age of a 11 and 7 years in addition to man and his wife and bus drivers egyptian nationality . 0.7647 -3.25376 Oracle : the victims were russian man and wife and his daughter of the eight years old from the age of a 11 and 7 years in addition to the man and his wife and bus drivers egyptian nationality young girls . 0.7964 -3.44128 MEMT

  11. Example IBM: the sri lankan prime minister criticizes head of the country's : 0.8862 ISI: The President of the Sri Lankan Prime Minister Criticized the President of the Country : 0.8660 CMU: Lankan Prime Minister criticizes her country: 0.6615 MEMT Sentence : Selected: the sri lankan prime minister criticizes president of the country . 0.9353 -3.27483 Oracle: the sri lankan prime minister criticizes president of the country's . 0.9767 -3.75805 MEMT

  12. Current System • Some features of decoding algorithm and final scoring still under experimentation • Initial development tests performed on TIDES 2003 Arabic-to-English MT data, using IBM, ISI and CMU SMT system output • Further development tests performed on Arabic-to-English EBMT Apptek and SYSTRAN system output and on three Chinese-to-English COTS systems • Integrated within CACI REFLEX Demonstration Platform MEMT

  13. Experimental Results:Chinese-to-English MEMT

  14. Experimental Results:Arabic-to-English MEMT

  15. Other Examples http://www-2.cs.cmu.edu/afs/cs/user/alavie/Students/Shyam/Comps100 MEMT

  16. Conclusions • New sentence-level MEMT approach with promising performance • Easy to run on both research and COTS systems • Tuning of parameter space for hypothesis generation – too tuned to METEOR? • Decoding is still suboptimal • Oracle scores show there is much room for improvement • Need for additional discriminant features • Some ideas currently under investigation MEMT

  17. Approach-1: Lattice MEMT • Approach: • Multiple MT systems produce a lattice of output segments • Create a “union” lattice of the various systems • Decode the joint lattice and select best synthetic output MEMT

  18. Approach-1: Lattice MEMT • Lattice Decoder from CMU’s SMT: • Lattice arcs are scored uniformly using word-to-word translation probabilities, regardless of which engine produced the arc • Decoder searches for path that optimizes combination of Translation Model score and Language Model score • Decoder can also reorder words or phrases (up to 4 positions ahead) MEMT

  19. Initial Experiment: Hindi-to-English Systems • Put together a scenario with “miserly” data resources: • Elicited Data corpus: 17589 phrases • Cleaned portion (top 12%) of LDC dictionary: ~2725 Hindi words (23612 translation pairs) • Manually acquired resources during the DARPA SLE: • 500 manual bigram translations • 72 manually written phrase transfer rules • 105 manually written postposition rules • 48 manually written time expression rules • No additional parallel text!! MEMT

  20. Initial Experiment: Hindi-to-English Systems • Tested on section of JHU provided data: 258 sentences with four reference translations • SMT system (stand-alone) • EBMT system (stand-alone) • XFER system (naïve decoding) • XFER system with “strong” decoder • No grammar rules (baseline) • Manually developed grammar rules • Automatically learned grammar rules • XFER+SMT with strong decoder (MEMT) MEMT

  21. Results on JHU Test Set (very miserly training data) MEMT

  22. Effect of Reordering in the Decoder MEMT

  23. Further Experiments:Arabic-to-English Systems • Combined: • CMU’s SMT system • CMU’s EBMT system • UMD rule-based system • (IBM didn’t work out) • TM scores from CMU SMT system • Built large new English LM • Tested on TIDES 2003 Test set MEMT

  24. Arabic-to-English SystemsLattice MEMT Results: MEMT

  25. Lattice MEMT • Main Drawbacks: • Requires MT engines to provide lattice output  difficult to obtain! • Lattice output from all engines must be compatible: common indexing based on source word positions  difficult to standardize! • Common TM used for scoring edges may not work well for all engines • Decoding does not take into account any reinforcements from multiple engines proposing the same translation for any portion of the input MEMT

  26. Demonstration MEMT

  27. Experimental Results:Arabic-to-English MEMT

More Related