We study a novel architecture for syntactic SMT. In contrast to the dominant approach in the literature, the system does not rely on translation rules, but treat translation as an unconstrained target sentence generation task, using soft features to capture lexical and syntactic correspondences between the source and target languages. Target syntax features and bilingual translation features ar...

„ Generative probability models deal with missing information and variable length sequences.

In reinforcement learning (RL), discriminative stimuli (S) allow agents to anticipate the value of a future outcome, and the response that will produce that outcome. We examined this processing by recording EEG locked to S during RL. Incentive value of outcomes and predictive value of S were manipulated, allowing us to discriminate between outcome-related and response-related activity. S predic...

Since recognition errors are unavoidable in speech recognition, confidence scoring, which accurately estimates the reliability of recognition results, is a critical function for speech recognition engines. In addition to achieving accurate confidence estimation, if we are to develop speech recognition systems that will be widely used by the public, speech recognition engines must be able to rep...

The introduction of low-cost RGB-D sensors has promoted the research in skeleton-based human action recognition. Devising a representation suitable for characterising actions on the basis of noisy skeleton sequences remains a challenge, however. We here provide two insights into this challenge. First, we show that the discriminative information of a skeleton sequence usually resides in a short ...

We present an online discriminative training approach to grapheme-to-phoneme (g2p) conversion. We employ a manyto-many alignment between graphemes and phonemes, which overcomes the limitations of widely used one-to-one alignments. The discriminative structure-prediction model incorporates input segmentation, phoneme prediction, and sequence modeling in a unified dynamic programming framework. T...

Discriminative canonical correlation analysis (DCCA) is a powerful supervised feature extraction technique for two sets of multivariate data, which has wide applications in pattern recognition. DCCA consists parts: (i) mean-centering that subtracts the sample mean from and (ii) solving generalized eigenvalue problem. The cost expensive when dealing with large number high-dimensional samples. To...