Regularizing Neural Machine Translation by Target-bidirectional Agreement

Although Neural Machine Translation (NMT) has achieved remarkable progress in the past several years, most NMT systems still suffer from a fundamental shortcoming as in other sequence generation tasks: errors made early in generation process are fed as inputs to the model and can be quickly amplified, harming subsequent sequence generation. To address this issue, we propose a novel model regularization method for NMT training, which aims to improve the agreement between translations generated by left-to-right (L2R) and right-to-left (R2L) NMT decoders. This goal is achieved by introducing two Kullback-Leibler divergence regularization terms into the NMT training objective to reduce the mismatch between output probabilities of L2R and R2L models. In addition, we also employ a joint training strategy to allow L2R and R2L models to improve each other in an interactive update process. Experimental results show that our proposed method significantly outperforms state-of-the-art baselines on Chinese-English and English-German translation tasks.Comment: Accepted by AAAI 201

SkillNet-X: A Multilingual Multitask Model with Sparsely Activated Skills

Traditional multitask learning methods basically can only exploit common knowledge in task- or language-wise, which lose either cross-language or cross-task knowledge. This paper proposes a general multilingual multitask model, named SkillNet-X, which enables a single model to tackle many different tasks from different languages. To this end, we define several language-specific skills and task-specific skills, each of which corresponds to a skill module. SkillNet-X sparsely activates parts of the skill modules which are relevant either to the target task or the target language. Acting as knowledge transit hubs, skill modules are capable of absorbing task-related knowledge and language-related knowledge consecutively. Based on Transformer, we modify the multi-head attention layer and the feed forward network layer to accommodate skill modules. We evaluate SkillNet-X on eleven natural language understanding datasets in four languages. Results show that SkillNet-X performs better than task-specific baselines and two multitask learning baselines (i.e., dense joint model and Mixture-of-Experts model). Furthermore, skill pre-training further improves the performance of SkillNet-X on almost all datasets. To investigate the generalization of our model, we conduct experiments on two new tasks and find that SkillNet-X significantly outperforms baselines

Minguo shi qi Zang shi wen ti Ying wen dang an xuan bian

Table of contents for "Minguo shi qi Zang shi wen ti Ying wen dang an xuan bian

Impact of industrial robot applications on global value chain participation of China manufacturing industry: Mediation effect based on product upgrading.

Promoting the application of industrial robot (IR) is an important module for China to build core competitiveness, and it is also the main grasp of global value chain participation (GVCP). Using China manufacturing industry panel data from 2006-2014, working from the perspective of product upgrading, this paper empirically analyzes the impact of IR applications on GVCP. The empirical results show that IR applications weaken China' incentives to participate in global value chains (GVCs); this weakening effect is reflected in both forward and backward participation in GVCs. On the one hand, the mediation effect test results indicate that the product upgrading effect brought about by IR applications can help China achieves the import substitution of intermediate inputs and uses local intermediate inputs to produce exports. These steps would reduce the backward participation in GVCs. On the other hand, the localization of manufacturing can result in China losing the opportunity to export intermediate inputs to other economies, thus reducing the forward participation of GVCs. Of course, due to sample limitations, the research conclusions of this article are only applicable to interpreting the Chinese economy

Resource-Aware Multi-Task Offloading and Dependency-Aware Scheduling for Integrated Edge-Enabled IoV

Internet of Vehicles (IoV) enables a wealth of modern vehicular applications, such as pedestrian detection, real-time video analytics, etc., that can help to improve traffic efficiency and driving safety. However, these applications impose significant resource demands on the in-vehicle resourceconstrained Edge Computing (EC) device installation. In this article, we study the problem of resource-aware offloading of these computation-intensive applications to the Closest roadside units (RSUs) or telecommunication base stations (BSs), where on-site EC devices with larger resource capacities are deployed, and mobility of vehicles are considered at the same time. Specifically, we propose an Integrated EC framework, which can keep edge resources running across various invehicles, RSUs and BSs in a single pool, such that these resources can be holistically monitored from a single control plane (CP). Through the CP, individual in-vehicle, RSU or BS edge resource availability can be obtained, hence applications can be offloaded concerning their resource demands. This approach can avoid execution delays due to resource unavailability or insufficient resource availability at any EC deployment. This research further extends the state-of-the-art by providing intelligent multi-task scheduling, by considering both task dependencies and heterogeneous resource demands at the same time. To achieve this, we propose FedEdge, a variant Bin-Packing optimization approach through Gang-Scheduling of multi-dependent tasks that co-schedules and co-locates multitask tightly on nodes to fully utilize available resources. Extensive experiments on real-world data trace from the recent Alibaba cluster trace, with information on task dependencies and resource demands, show the effectiveness, faster executions, and resource efficiency of our approach compared to the existing approaches

A Wide Range Distributed Multi-Node Receive Chain Calibration Method Based on GNSS Software Receiver

The phase calibration of distributed multi-node receiving system is required considering the phase synthesis working mode of distributed radar system for echo signals. Whereas, in respect to the traditional calibration tower and unmanned aerial vehicle scheme, there are many disadvantages. The wide range distributed multi-node receive chain calibration method based on GNSS software receiver was proposed in this paper, taking advantage of the software receiver to calculate and obtain the actual phase difference among the receiving nodes as well as the coordinate information of satellites in transmitting signal. Therefore, the phase difference resulted from different transmit path can be deleted, and the phase inconsistencies among the receiving nodes can be obtained. Besides, the fingerprint maps database regarding inconsistencies among nodes and two-dimensional angle of relative receiving node for satellites was constructed in this paper, the correspondence between the omnidirectional two-dimensional angle and the inconsistencies among different nodes was established by the actual results and two-dimensional fitting, and the phase inconsistency results among nodes can be obtained only by solving the satellite coordinate information. There are many advantages for the method proposed in this paper, such as slight influence of multipath effect, capability of all-weather all-day calibration, low costs