Обучение с минимальной ошибкой

We address the problem of training the free parameters of a statistical machine transla- tion system. We show significant improve- ments over a state-of-the-art minimum er- ror rate training baseline …...

We describe the experiments of the UC Berke- ley team on improving English-Spanish ma- chine translation of news text, as part of the WMT’08 Shared Translation Task. We ex- periment …...

Minimum error rate training (MERT) is a widely used learning procedure for statistical machine translation models. We contrast three search strategies for MERT: Powell’s method, the variant of coordinate descent …...

Minimum error rate training (MERT) in- volves choosing parameter values for a machine translation (MT) system that maximize performance on a tuning set as measured by an automatic evaluation met- …...

We describe Joshua, an open source toolkit for statistical machine transla- tion. Joshua implements all of the algo- rithms required for synchronous context free grammars (SCFGs): chart-parsing, n- gram language …...

We propose a language-independent approach for improving statistical machine translation for morphologically rich languages using a hybrid morpheme-word representation where the basic unit of translation is the morpheme, but word …...

While Inversion Transduction Grammar (ITG) has regained more and more attention in recent years, it still suffers from the major obstacle of speed. We propose a discriminative ITG prun- ing …...

Minimum Error Rate Training is the algo- rithm for log-linear model parameter train- ing most used in state-of-the-art Statistical Machine Translation systems. In its original formulation, the algorithm uses N-best …...

Weights of the various components in a standard Statistical Machine Translation model are usually estimated via Minimum Error Rate Training. With this, one finds their optimum value on a development …...

Structured perceptrons are attractive due to their simplicity and speed, and have been used successfully for tuning the weights of binary features in a machine translation system. In attempting to …...

Minimum error rate training is often the pre- ferred method for optimizing parameters of statistical machine translation systems. MERT minimizes error rate by using a surrogate rep- resentation of the …...

In statistical machine translation, minimum error rate training (MERT) is a standard method for tuning a single weight with regard to a given development data. However, due to the diversity …...

Minimum Error Rate Training (MERT) re- mains one of the preferred methods for tun- ing linear parameters in machine translation systems, yet it faces significant issues. First, MERT is an …...

A typical knowledge-based question an- swering (KB-QA) system faces two chal- lenges: one is to transform natural lan- guage questions into their meaning repre- sentations (MRs); the other is to …...

Most modern statistical machine translation systems are based on linear statistical models. One extremely effective method for estimating the model parameters is minimum error rate training (MERT), which is an …...