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

Temporary Structures in Shangai EXPO2010 - Structural Design Specification and Example

p. 790-798Many temporary buildings and structures are constructed in the Expo Park, Shanghai as pavilions and facilities of EXPO2010. New materials and new structural systems are expected to be used and appear. The structural design specification for temporary structures of EXPO2010 is summarized and the structural design of Norwegian pavilion is described in this paper.Wu, M.; Meng, L.; Mu, T. (2010). Temporary Structures in Shangai EXPO2010 - Structural Design Specification and Example. Editorial Universitat Politècnica de València. http://hdl.handle.net/10251/694