Chunk-Based Bi-Scale Decoder for Neural Machine Translation

In typical neural machine translation~(NMT), the decoder generates a sentence word by word, packing all linguistic granularities in the same time-scale of RNN. In this paper, we propose a new type of decoder for NMT, which splits the decode state into two parts and updates them in two different time-scales. Specifically, we first predict a chunk time-scale state for phrasal modeling, on top of which multiple word time-scale states are generated. In this way, the target sentence is translated hierarchically from chunks to words, with information in different granularities being leveraged. Experiments show that our proposed model significantly improves the translation performance over the state-of-the-art NMT model.Comment: Accepted as a short paper by ACL 201

Conservation of Natural and Cultural Heritage in the Huong Son Complex of Natural Beauty and Historical Monuments, Northern Vietnam

The Huong Son Complex has long been recognised as an important sacred landscape in Vietnam due to its spiritual and cultural values. The area also retains many aesthetic and biological values. Unfortunately, its cultural and natural treasures are currently at risk due to anthropogenic impacts, mainly associated with increased spiritual tourist activities. Some urgent solutions have been implemented, but they give priority to protecting cultural values and sometimes conflict with nature conservation efforts. This problem was encountered during our recent bat conservation research in Huong Son. Our preliminary findings revealed symbiotic relationships between natural and cultural heritage in Huong Son; thus, linking nature and culture in conservation planning and management is critical for the sustainable development of the site. However, the application of this approach in Huong Son, and other sacred places in Vietnam, is challenged by gaps in basic research and the inadequate attention of local stakeholders

The effect of psychological collectivism on creative work involvement: The role of prosocial motivation and leadership style

Collectivism is a common concept in the cultural study, which is used to describe a community whose motivation and emphasis are strongly tight to collective perspective. Considering previous research's suggestions on another level of analysis of collectivism, and limitations of the cultural-based view of collectivism in studying organizational behaviors, this thesis examined collectivism at the individual level as a personality trait, termed psychological collectivism. The prior research investigated several outcomes of psychological collectivism. Out of them, creative work involvement has been neglected. Given the importance of innovation process, in which creative engagement is essential, in the current knowledge-based economy, and advantages of the collective attitude in facilitating group-based project works, the relationship between psychological collectivism and creative work involvement should be explored. This thesis objective is to study (1) the effect of psychological collectivism on creative work involvement, (2) the mediating role of prosocial motivation in this relationship, and (3) the moderating effect of three different leadership styles (i.e. transformational leadership, transactional leadership, laissez-faire) on the psychological collectivism-creative work involvement relationship. To address these objectives, this thesis utilized the quantitative approach. A sample of 167 organizational members working in different organizations was studied. The data were collected by the online web-based questionnaire, and all the variables were measured at the same point of time. After that, Structural Equation Modeling (SEM) method is used to analyze the data. The results indicated that psychological collectivism is positively related to creative work involvement; prosocial motivation partially mediates this relationship; transactional leadership marginally weakens the psychological collectivism-creative work involvement relationship; whereas, transformational leadership and laissez-faire show no significant moderating effect. The research findings suggested that employees who have a stronger orientation to work in groups will devote more time and effort to creative processes associated with work, and one of the mechanisms explained this motive is through employee's motivation to help others. Also, leaders who strongly emphasize on results, and use rewards as motivators for employees' job accomplishment may undermine their willingness to carry out creative activities. These make several implications for management practices such as a proper human resource allocation for innovation projects, customized training programs, and suitable leadership behaviors to different groups of employees

Membrane-associated collagens with interrupted triple-helices (MACITs):evolution from a bilaterian common ancestor and functional conservation <i>in C. elegans</i>

Protein sequence alignment of human collagens XIII, XXIII, XXV and six alternative spliced variants of COL-99. The protein sequence of the newly identified COL-99f was compared with the other COL-99 variants and human collagens XIII, XXIII and XXV. Putative furin cleavage residues in these proteins and the peptides for producing the COL-99 antibodies AB5625.11 and AB693 are highlighted in the sequence. (PDF 22 kb

A Novel Approach to Dropped Pronoun Translation

Dropped Pronouns (DP) in which pronouns are frequently dropped in the source language but should be retained in the target language are challenge in machine translation. In response to this problem, we propose a semisupervised approach to recall possibly missing pronouns in the translation. Firstly, we build training data for DP generation in which the DPs are automatically labelled according to the alignment information from a parallel corpus. Secondly, we build a deep learning-based DP generator for input sentences in decoding when no corresponding references exist. More specifically, the generation is two-phase: (1) DP position detection, which is modeled as a sequential labelling task with recurrent neural networks; and (2) DP prediction, which employs a multilayer perceptron with rich features. Finally, we integrate the above outputs into our translation system to recall missing pronouns by both extracting rules from the DP-labelled training data and translating the DP-generated input sentences. Experimental results show that our approach achieves a significant improvement of 1.58 BLEU points in translation performance with 66% F-score for DP generation accuracy

Controllable Motion Synthesis and Reconstruction with Autoregressive Diffusion Models

Data-driven and controllable human motion synthesis and prediction are active research areas with various applications in interactive media and social robotics. Challenges remain in these fields for generating diverse motions given past observations and dealing with imperfect poses. This paper introduces MoDiff, an autoregressive probabilistic diffusion model over motion sequences conditioned on control contexts of other modalities. Our model integrates a cross-modal Transformer encoder and a Transformer-based decoder, which are found effective in capturing temporal correlations in motion and control modalities. We also introduce a new data dropout method based on the diffusion forward process to provide richer data representations and robust generation. We demonstrate the superior performance of MoDiff in controllable motion synthesis for locomotion with respect to two baselines and show the benefits of diffusion data dropout for robust synthesis and reconstruction of high-fidelity motion close to recorded data