Bayesian Optimisation for Machine Translation
This work addresses the challenge of improving translation accuracy and speed for machine translation practitioners, representing an incremental advancement in optimization techniques.
The paper tackles the problem of efficiently optimizing statistical machine translation systems by introducing novel Bayesian optimization algorithms, resulting in faster convergence and lower error rates compared to existing methods.
This paper presents novel Bayesian optimisation algorithms for minimum error rate training of statistical machine translation systems. We explore two classes of algorithms for efficiently exploring the translation space, with the first based on N-best lists and the second based on a hypergraph representation that compactly represents an exponential number of translation options. Our algorithms exhibit faster convergence and are capable of obtaining lower error rates than the existing translation model specific approaches, all within a generic Bayesian optimisation framework. Further more, we also introduce a random embedding algorithm to scale our approach to sparse high dimensional feature sets.