Researchers who lead the trends

Xuan-Hung Doan, Phuong-Tram T. Nguyen, Viet-Phuong La, Hong-Kong T. Nguyen (2019). Chapter 5. Researchers who lead the trends. In Quan-Hoang Vuong, Trung Tran (Eds.), The Vietnamese Social Sciences at a Fork in the Road (pp. 98–120). Warsaw, Poland: De Gruyter. DOI:10.2478/9783110686081-010 Online ISBN: 9783110686081 © 2019 Sciend

Business model assessment in faecal sludge management in selected Vietnamese cities

An assessment of business models in faecal sludge management (FSM) in 3 largest Vietnamese cities of Hanoi, Hai Phong, and Ho Chi Minh has been conducted through interview of 1,000 households and survey at 20 FS emptying operators. The public enterprises keep a significant market share for FSM. However, these enterprises run FSM business as a ‘‘must do” activity, which is subsidized by the city’s budget. In order to reduce operation costs and to make benefit, most of private enterprises are practicing illegal FS dumping. Financial status of private enterprises looks less optimistic in scenarios where FS is required to be brought to landfill or composting plant. A number of enterprises would not find capital recovery within 5 years, and some others would face loss. For sustainable FSM business, costs for adequate FSM should be recovered, while regulatory support and coordination role of local authorities are needed

Understanding the interplay of lies, violence, and religious values in folktales

This research employs the Bayesian network modeling approach, and the Markov chain Monte Carlo technique, to learn about the role of lies and violence in teachings of major religions, using a unique dataset extracted from long-standing Vietnamese folktales. The results indicate that, although lying and violent acts augur negative consequences for those who commit them, their associations with core religious values diverge in the outcome for the folktale characters. Lying that serves a religious mission of either Confucianism or Taoism (but not Buddhism) brings a positive outcome to a character. A violent act committed to serving Buddhist mission results in a happy ending for the committer

Improving Machine Translation Quality with Denoising Autoencoder and Pre-Ordering

The problems in machine translation are related to the characteristics of a family of languages, especially syntactic divergences between languages. In the translation task, having both source and target languages in the same language family is a luxury that cannot be relied upon. The trained models for the task must overcome such differences either through manual augmentations or automatically inferred capacity built into the model design. In this work, we investigated the impact of multiple methods of differing word orders during translation and further experimented in assimilating the source languages syntax to the target word order using pre-ordering. We focused on the field of extremely low-resource scenarios. We also conducted experiments on practical data augmentation techniques that support the reordering capacity of the models through varying the target objectives, adding the secondary goal of removing noises or reordering broken input sequences. In particular, we propose methods to improve translat on quality with the denoising autoencoder in Neural Machine Translation (NMT) and pre-ordering method in Phrase-based Statistical Machine Translation (PBSMT). The experiments with a number of English-Vietnamese pairs show the improvement in BLEU scores as compared to both the NMT and SMT systems