We present a statistical phrase-based translation model that uses hierarchical phrases— phrases that contain subphrases. The model is formally a synchronous context-free grammar but is learned from a bitext without any syntactic information. Thus it can be seen as a shift to the formal machinery of syntaxbased translation systems without any linguistic commitment. In our experiments using BLEU ...

In recent years stochastic context-free grammars have been shown to be effective in modeling human activities because of the hierarchical structures they represent. However, most of the research in this area has yet to address the issue of learning the activity grammars from a noisy input source, namely, video. In this paper, we present a framework for identifying noise and recovering the basic...

We present a class trigram language model in which each class is specified by a probabilistic context-free grammar. We show how to estimate the parameters of the model, and how to smooth these estimates. Experimental perplexity and speech recognition results are presented.

We present a model of global processing difficulty in human parsing. This model is based on a probabilistic context-free grammar and is trained on a realistic corpus sample. It achieves broad coverage and good parsing accuracy on unseen text, and its predictions are significantly correlated with experimental data on word order preferences in German. The model makes predictions about the differe...

Automotive infotainment systems now provide drivers the ability to hear incoming Short Message Service (SMS) text messages using text-to-speech. However, the question of how best to allow users to respond to these messages using speech recognition remains unsettled. In this paper, we propose a robust voice search approach to replying to SMS messages based on template matching. The templates are...

Word reordering is a crucial technique in statistical machine translation in which syntactic information plays an important role. Synchronous context-free grammar has typically been used for this purpose with various modifications for adding flexibilities to its synchronized tree generation. We permit further flexibilities in the synchronous context-free grammar in order to translate between la...

Probabilistic synchronous context-free grammar (PSCFG) translation models define weighted transduction rules that represent translation and reordering operations via nonterminal symbols. In this work, we investigate the source of the improvements in translation quality reported when using two PSCFG translation models (hierarchical and syntax-augmented), when extending a state-of-the-art phraseb...

In this paper, we represent human actions as short sequences of atomic body poses. The knowledge of body pose is stored only implicitly as a set of silhouettes seen from multiple viewpoints; no explicit 3D poses or body models are used, and individual body parts are not identified. Actions and their constituent atomic poses are extracted from a set of multiview multiperson video sequences by an...

The use of the Inside-Outside (IO) algorithm for the estimation of the probability distributions of Stochastic Context-Free Grammars (SCFGs) in Natural-Language processing is restricted due to the time complexity per iteration and the large number of iterations that it needs to converge. Alternatively, an algorithm based on the Viterbi score (VS) is used. This VS algorithm converges more rapidl...