Decoding is a core process of the statistical machine translation, and determines the final results of it. In this paper, a decoding optimization for Chinese-English SMT with a dependent syntax language model was proposed, in order to improve the performance of the decoder in Chinese-English statistical machine translation. The data set was firstly trained in a dependent language model, and the...

We introduce PRISM (Power-efficient, Robust, hIgh-compression, Syndrome-based Multimedia coding), a video coding paradigm underpinned by the principles of distributed source coding from multi-user information theory. PRISM, which rests heavily on channel coding concepts, represents a radical departure from current state-of-the-art video coding architectures, including those driving popular stan...

Error concealment techniques are useful in video transmission over channels that introduce bit errors. The efficiency and result of error concealment technique, however, rely on the error detection capabilities of video decoders. A novel error detection technique employing fragile watermarking is proposed in this paper. By embedding a fragile watermark on the quantized DCT coefficients and exam...

Machine translation is widely used in people’s daily lives and production, occupying an important position. In order to improve the accuracy of literary intelligent translation, research on based improved optimization model. Based semantic features, ontology model including encoder a decoder created by machine translation. literature model, conversion layer, forward neural network residual conn...

The quantum-dot cellular automata (QCA) is considered as an alternative tocomplementary metal oxide semiconductor (CMOS) technology based on physicalphenomena like Coulomb interaction to overcome the physical limitations of thistechnology. The decoder is one of the important components in digital circuits, whichcan be used in more comprehensive circuits such as full adde...

Most neural machine translation (NMT) models are based on the sequential encoder-decoder framework, which makes no use of syntactic information. In this paper, we improve this model by explicitly incorporating source-side syntactic trees. More specifically, we propose (1) a bidirectional tree encoder which learns both sequential and tree structured representations; (2) a tree-coverage model tha...

Deep generative models have been enjoying success in modeling continuous data. However it remains challenging to capture the representations for discrete structures with formal grammars and semantics. How to generate both syntactically and semantically correct data still remains largely an open problem. Inspired by the theory of compiler where syntax and semantics check is done via syntax-direc...

We present progress on Joshua, an opensource decoder for hierarchical and syntaxbased machine translation. The main focus is describing Thrax, a flexible, open source synchronous context-free grammar extractor. Thrax extracts both hierarchical (Chiang, 2007) and syntax-augmented machine translation (Zollmann and Venugopal, 2006) grammars. It is built on Apache Hadoop for efficient distributed p...

This paper presents a method to integrate multiple reordering strategies in phrase-based statistical machine translation. Recently there has been much research effort in reordering problems in machine translation. State-of-the-art decoders incorporate sophisticated local reordering strategies, but there is little research on a unified approach to incorporate various kinds of reordering methods....