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Understanding SMT without the “S” (Statistics)

Understanding SMT without the “S” (Statistics). Robert Frederking. Statistical modelling. Think about statistical modelling as fitting a curve to data points Start with parameterized function, error metric, and data points

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Understanding SMT without the “S” (Statistics)

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  1. Understanding SMT without the “S”(Statistics) Robert Frederking

  2. Statistical modelling Think about statistical modelling as fitting a curve to data points Start with parameterized function, error metric, and data points After fitting the function to data using parameters, you can make predictions

  3. y = a*x + b Err = sqrt(sum(di^2))

  4. y = a*x + b Y X

  5. y = a*x + b Err = sqrt(sum(di^2))

  6. y = a*x + b Y?? X

  7. Y2 (Y-y0)^2/a + (X-x0)^2/b = r^2 Y1 Err = sqrt(sum(di^2)) X

  8. Statistical modelling • Think about statistical modelling as fitting a curve to data points • Parameterized function, error metric, data points • After fitting parameters, you can make predictions • But you will get some fit for any data set • Human researchers need to come up with “good” family of functions, and error metric, for the data you see • Want low error number, good predictions • Tractable, both in training and decoding • including data availability, sparseness issues

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