Extending BLEU MT Evaluation Method with Frequency Weighting

We present the results of an experiment on extending the automatic method of Machine Translation evaluation BLUE with statistical weights for lexical items, such as tf.idf scores. We show that this extension gives additional information about evaluated texts; in particular it allows us to measure translation Adequacy, which, for statistical MT systems, is often overestimated by the baseline BLEU method. The proposed model uses a single human reference translation, which increases the usability of the proposed method for practical purposes. The model suggests a linguistic interpretation which relates frequency weights and human intuition about translation Adequacy and Fluency.