Jay Ponte

A Systematic Comparison of Phrase-Based, Hierarchical and Syntax-Augmented Statistical MT

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 phrase-based baseline that serves as the lexical support for both PSCFG models. We isolate the impact on translation quality for several important design decisions in each model. We perform this comparison on three NIST language translation tasks; Chinese-to-English, Arabic-to-English and Urdu-to-English, each representing unique challenges.

Sparse coding of auditory features for machine hearing in interference

A key problem in using the output of an auditory model as the input to a machine-learning system in a machine-hearing application is to find a good feature-extraction layer. For systems such as PAMIR (passive-aggressive model for image retrieval) that work well with a large sparse feature vector, a conversion from auditory images to sparse features is needed. For audio-file ranking and retrieval from text queries, based on stabilized auditory images, we took a multi-scale approach, using vector quantization to choose one sparse feature in each of many overlapping regions of different scales, with the hope that in some regions the features for a sound would be stable even when other interfering sounds were present and affecting other regions. We recently extended our testing of this approach using sound mixtures, and found that the sparse-coded auditory-image features degrade less in interference than vector-quantized MFCC sparse features do. This initial success suggests that our hope of robustness in interference may indeed be realizable, via the general idea of sparse features that are localized in a domain where signal components tend to be localized or stable.