Sentence examples for training sentences from inspiring English sources

For five training sentences, JGMM leads to lower ND values (except for F2M), however, using 10 training sentences, the proposed GB achieves lower or very similar ND values.

Subjects were presented with four training sentences before the test started.

In terms of ND, JGMM leads to lower ND values using 5 training sentences.

The source and target training sentences are assumed to be parallel and phonetically labeled.

Conversion results reported by the authors were obtained using several hundreds of parallel training sentences.

This supports the idea of using a probabilistic model for the errors over training sentences.

Each training sentence is labeled as "1" for a spoken class and "2" for a written class.

The RNNLM was trained using sentences extracted from the WSJ text corpora [28] distributed by the Linguistic Data Consortium LDCC), while ensuring that the sentences in the evaluation and development sets were not employed.

The first set was used for training the sentence classification component and for devising the hand-crafted extraction rules for the extraction component.

This means that training on sentence pairs will result in dynamically created lexicons containing both word forms (inflections) and lemmas (base forms) as well as data on parts-of-speech and function correspondences from those sentences.

Table 2 Distribution of classes in the training data   # % Sentences with inclusive pronouns 1395 12.9 Sentences without inclusive pronouns 9446 87.1 Total 10,841  .