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RESEARCH PRODUCT
Particle Swarm Optimization as a New Measure of Machine Translation Efficiency
Jolanta Mizera-pietraszkoRicardo Rodríguez JorgeJosé Angel Montes OlguínEdgar A. Martinez Garciasubject
Machine translationComputer scienceComputer Science::Neural and Evolutionary ComputationCosine similarityEvolutionary algorithmParticle swarm optimizationComputer Science::Computation and Language (Computational Linguistics and Natural Language and Speech Processing)020206 networking & telecommunications02 engineering and technologyTranslation (geometry)computer.software_genreEvolutionary algorithmsSet (abstract data type)IdentifierMachine Translation0202 electrical engineering electronic engineering information engineeringBenchmark (computing)020201 artificial intelligence & image processingCosine similarityAlgorithmcomputerdescription
The present work proposes a new approach to measuring efficiency of evolutionary algorithm-based Machine Translation. We implement some attributes of evolutionary algorithms performing cosine similarity objective function of a Particle Swarm Optimization (PSO) algorithm then, we evaluate an English text set for translation precision into the Spanish text as a simulated benchmark, and explore the backward process. Our results show that PSO algorithm can be used for translation of multiple language sentences with one identifier only, in other words the technology presented is language-pair independent. Specifically, we indicate that our cosine similarity objective function improves the velocity attribute of the PSO algorithm, making the complex cost functions unnecessary.
| year | journal | country | edition | language |
|---|---|---|---|---|
| 2018-01-01 |