While phrase-based statistical machine translation systems prefer to translate with longer phrases, this may cause errors in a free word order language, such as Japanese, in which the order of the arguments of the predicates is not solely determined by the predicates and the arguments can be placed quite freely in the text. In this paper, we propose to reorder the arguments but not the predicates in Japanese using a dependency structure as a kind of reordering. Instead of a single deterministically given permutation, we generate multiple reordered phrases for each sentence and translate them independently. Then we apply a re-ranking method using a discriminative approach by Ranking Support Vector Machines (SVM) to re-score the multiple reordered phrase translations. In our experiment with the travel domain corpus BTEC, we gain a 1.22% BLEU score improvement when only 1-best is used for re-ranking and 4.12% BLEU score improvement when n-best is used for Japanese-English translation.

Blood pressure measurement by auscultatory method is a compulsory skill that is required by all healthcare practitioners. During the measurement, they must concentrate on recognizing the Korotkoff sounds, looking at the sphygmomanometer scale, and constantly deflating the cuff pressure simultaneously. This complex operation is difficult for the new learners and they need a lot of practice with the supervisor in order to guide them on their measurements. However, the supervisor is not always available and consequently, they always face the problem of lack of enough training. In order to help them mastering the skill of measuring blood pressure by auscultatory method more efficiently and effectively, we propose using a sensor device to capture the signals of Korotkoff sounds and cuff pressure during the measurement, and display the signal changes on a visualization tool through wireless connection. At the end of the measurement, the learners can verify their skill on deflation speed and recognition of Korotkoff sounds using the graphical view, and compare their measurements with the machine instantly. By using this device, the new learners do not need to wait for their supervisor for training but can practice with their colleagues more frequently. As a result, they will be able to acquire the skill in a shorter time and be more confident with their measurements.

We present a method to constrain a statistical generative word alignment model with the output from a discriminative model. The discriminative model is trained using a small set of hand-aligned data that ensures higher precision in alignment. On the other hand, the generative model improves the recall of alignment. By combining these two models, the alignment output becomes more suitable for use in developing a translation model for a phrase-based statistical machine translation (SMT) system. Our experimental results show that the joint alignment model improves the translation performance. The improvement in average of BLEU and METEOR scores is around 1.0-3.9 points.