Amir Kamran

Results of the WMT15 Tuning Shared Task

This paper presents the results of the WMT15 Tuning Shared Task. We provided the participants of this task with a complete machine translation system and asked them to tune its internal parameters (feature weights). The tuned systems were used to translate the test set and the outputs were manually ranked for translation quality. We received 4 submissions in the English-Czech and 6 in the Czech-English translation direction. In addition, we ran 3 baseline setups, tuning the parameters with standard optimizers for BLEU score.

English to Urdu Statistical Machine Translation: Establishing a Baseline

The aim of this paper is to categorize and present the existence of resources for Englishto-Urdu machine translation (MT) and to establish an empirical baseline for this task. By doing so, we hope to set up a common ground for MT research with Urdu to allow for a congruent progress in this field. We build baseline phrase-based MT (PBMT) and hierarchical MT systems and report the results on 3 official independent test sets. On all test sets, hierarchial MT significantly outperformed PBMT. The highest single-reference BLEU score is achieved by the hierarchical system and reaches 21.58% but this figure depends on the randomly selected test set. Our manual evaluation of 175 sentences suggests that in 45% of sentences, the hierarchical MT is ranked better than the PBMT output compared to 21% of sentences where PBMT wins, the rest being equal.

Results of the WMT16 Tuning Shared Task.

This paper presents the results of the WMT16 Tuning Shared Task. We provided the participants of this task with a complete machine translation system and asked them to tune its internal parameters (feature weights). The tuned systems were used to translate the test set and the outputs were manually ranked for translation quality. We received 4 submissions in the Czech-English and 8 in the English-Czech translation direction. In addition, we ran 2 baseline setups, tuning the parameters with standard optimizers for BLEU score. In contrast to previous years, the tuned systems in 2016 rely on large data.