School effectiveness research in China

The major purpose of this study was to identify the processes of effective schools in China, thereby enriching the international study of school effectiveness. A multistrand concurrent mixed model design was utilized to test the research hypotheses and answer the research questions. Both probability and purposive sampling strategies were employed in this study. The MANOVA analyses from the teacher, student, and parent questionnaires revealed a consistent result: there were significant differences between more effective and less effective schools across all the traditional school effectiveness variables. The MANOVA results for classroom teaching also demonstrated significant differences between more effective and less effective schools across nine traditional teacher effectiveness variables. This study revealed many differences in the processes of effective schooling in China as opposed to those described in the international literature. These differences included the importance of the role of the Banzhuren (the director of a class), the overriding importance of students’ test scores in teacher evaluation, the impact of large class sizes, and the impact of inadequate facilities and resources especially in the rural areas. Results regarding effective schooling in urban areas in China (as opposed to rural areas) are that the major differences center on faculty participation in decision making, expectations for students (especially future expectations), opportunities for teachers\u27 professional development, and so forth. This study also revealed many differences in the processes of effective teaching in China as opposed to those described in the international literature. For example, Chinese teaching behaviors are very uniform (relatively small variance across classrooms), Chinese classes emphasize whole class activities more than small group activities, teachers are very strict with students in both discipline and studies, and demonstration lessons are very popular both within and across schools

Foreign Direct Investment and Regional Inequality in China

China?s economic miracle over the past three decades has been featured with its open-door policy, especially the absorption of foreign capital. One downside effect of economic reform has been the ever rising interregional inequality. As FDI is highly unevenly distributed across regions, many scholars and policymakers have blamed their inflows as one of the main factors driving the Chinese regions apart. If this logic were true, then controlling the scale of FDI could be a solution to reduce regional inequality. However, it is difficult to reconcile the positive effect of FDI on economic growth with its potential ?negative? effect on regional inequality. This is a controversial and provocative issue in the economic development literature. Using the largest panel dataset covering all the Chinese regions over the entire period 1979-2003 and employing an augmented Cobb-Douglas production function, this paper proves that FDI has been an important factor responsible for regional growth differences in China. However, it suggests that FDI cannot be blamed for causing regional inequality; it is the uneven distribution of FDI instead of FDI itself that has caused regional growth differences. The key policy issue is that FDI should be guided towards the inland areas with preferential policies in order to improve the spatial allocation of investments as a means to reduce regional inequality.foreign direct investment, regional inequality, China

Foreign direct investment and regional inequality in China

One downside effect of rapid economic growth in China has been the ever rising inter-regional inequality. Foreign direct investment (FDI) has been blamed for driving the Chinese regions apart. It is difficult to reconcile the positive effect of FDI on economic growth with its potential ‘negative’ effect on regional inequality. Using the largest panel dataset for the Chinese regions over 1979-2003 and employing an augmented Cobb-Douglas production function, this paper proves that FDI has been an important factor of economic growth in China. It also suggests that it is the uneven distribution of FDI instead of FDI itself that has caused regional growth differences.Foreign direct investment, regional inequality, China

Unsupervised Neural Machine Translation with SMT as Posterior Regularization

Without real bilingual corpus available, unsupervised Neural Machine Translation (NMT) typically requires pseudo parallel data generated with the back-translation method for the model training. However, due to weak supervision, the pseudo data inevitably contain noises and errors that will be accumulated and reinforced in the subsequent training process, leading to bad translation performance. To address this issue, we introduce phrase based Statistic Machine Translation (SMT) models which are robust to noisy data, as posterior regularizations to guide the training of unsupervised NMT models in the iterative back-translation process. Our method starts from SMT models built with pre-trained language models and word-level translation tables inferred from cross-lingual embeddings. Then SMT and NMT models are optimized jointly and boost each other incrementally in a unified EM framework. In this way, (1) the negative effect caused by errors in the iterative back-translation process can be alleviated timely by SMT filtering noises from its phrase tables; meanwhile, (2) NMT can compensate for the deficiency of fluency inherent in SMT. Experiments conducted on en-fr and en-de translation tasks show that our method outperforms the strong baseline and achieves new state-of-the-art unsupervised machine translation performance.Comment: To be presented at AAAI 2019; 9 pages, 4 figure

Joint Training for Neural Machine Translation Models with Monolingual Data

Monolingual data have been demonstrated to be helpful in improving translation quality of both statistical machine translation (SMT) systems and neural machine translation (NMT) systems, especially in resource-poor or domain adaptation tasks where parallel data are not rich enough. In this paper, we propose a novel approach to better leveraging monolingual data for neural machine translation by jointly learning source-to-target and target-to-source NMT models for a language pair with a joint EM optimization method. The training process starts with two initial NMT models pre-trained on parallel data for each direction, and these two models are iteratively updated by incrementally decreasing translation losses on training data. In each iteration step, both NMT models are first used to translate monolingual data from one language to the other, forming pseudo-training data of the other NMT model. Then two new NMT models are learnt from parallel data together with the pseudo training data. Both NMT models are expected to be improved and better pseudo-training data can be generated in next step. Experiment results on Chinese-English and English-German translation tasks show that our approach can simultaneously improve translation quality of source-to-target and target-to-source models, significantly outperforming strong baseline systems which are enhanced with monolingual data for model training including back-translation.Comment: Accepted by AAAI 201