In this paper, experimental results of evaluating dialogue strategies of confirmation with a noisy channel are presented. First, the types of errors in task-oriented dialogues are investigated and classified as communication, dialogue, knowledge, problem solving, or objective errors. Since the errors are of different levels, the methods for recovering from errors must be examined separately. We have investigated that the dialogue and knowledge errors generated by communication errors can be recovered through system confirmation with the user. In addition, we examined that the manner in which a system initiates dialogue, namely, dialogue strategies, might influence the cooperativity of their interactions depending on the frequency of confirmations and the amount of information conveyed. Furthermore, the choice of dialogue strategies will be influenced by the rate of occurrence of communication errors in a communication channel and related to the properties of the task, for example, the difficulty in achieving a goal or the frequency of the movement of initiatives. To verify these hypotheses, we prepared a testbed task, the Group Scheduling Task, and examined it through a computer-to-computer dialogue simulation in which one system took the part of a scheduling system and the other system acted as a user. In this simulation, erroneous input for the scheduling system was also developed. The user system was designed to act randomly so that it could simulate a real human user, while the scheduling system was devised to strictly follow a particular dialogue strategy of confirmation. The experimental results showed that a certain amount of confirmation was required to overcome errors when the rate of occurrence of communication errors was high, but that excessive confirmation did not serve to resolve errors, depending on the task involved.

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.

This paper describes a distributed video transcoding system that can simultaneously transcode an MPEG-2 video file into various video coding formats with different rates. The transcoder divides the MPEG-2 file into small segments along the time axis and transcodes them in parallel. Efficient video segment handling methods are proposed that minimize the inter-processor communication overhead and eliminate temporal discontinuities from the re-encoded video. We investigate how segment transcoding should be distributed to obtain the shortest total transcoding time. Experimental results show that implementing distributed transcoding on 10 PCs can decrease the total transcoding time by a factor of about 7 for single transcoding and by a factor of 9.5 for simultaneous three kinds of transcoding rates.

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.

With the rapid deployment of the Internet of Things, where various devices are connected to communication networks, remote driving applications for Unmanned Vehicles (UVs) are attracting attention. In addition to automobiles, autonomous driving technology is expected to be applied to various types of equipment, such as small vehicles equipped with surveillance cameras to monitor building internally and externally, autonomous vehicles that deliver office supplies, and wheelchairs. When a UV is remotely controlled, the control accuracy deteriorates due to transmission delay and jitter. The accuracy must be kept high to realize UV control system by a cloud server. In this study, we investigate the effectiveness of Digital Twin Computing (DTC) for path tracking control of a UV. We show the results of simulations that use transmission delay values measured on the Internet with some cloud servers. Through the results, we quantitatively clarify that application of DTC improves control accuracy on path tracking control. We also clarify that application of jitter buffer, which absorbs the transmission delay fluctuation, can further improve the accuracy.