View Seminar Video Abstract Generating usable internal representations of speech input requires, among other operations, fractionating the signals into temporal units/chunks of the appropriate granularity. Adopting (and adapting) Marr’s (1982) approach to vision, a perspective[…]
View Seminar Video Abstract Over the last decade, linear models have become the standard machine learning approach for supervised classification, ranking, and structured prediction natural language processing. They can handle very high-dimensional problem representations, they[…]
View Seminar Video Abstract Be it in a restaurant or other reverberant and noisy environment, normal hearing listeners segregate multiple sources, usually strongly overlapping in frequency, well beyond capabilities expected by current beamforming approaches. What[…]
View Seminar Video Abstract As we scale statistical machine translation systems to general domain, we face many challenges. This talk outlines two approaches for building better broad-domain systems.First, progress in data-driven translation is limited by[…]
Abstract For a canonical primary cortical neuron which we call N. we introduce a mathematically tractable and neuroscientifically meaningful model of how N stochastically converts the excitation intensities it receives from the union of all[…]
Abstract The use of Active Learning (AL) to reduce NLP annotation costs has recently generated considerable interest. There has also been considerable interest in dealing effectively with the class imbalance that NLP problems so often[…]
View Seminar Video Abstract I’ll present our work at IBM on word-alignment algorithms trained using supervised corpora. Also, I’ll demonstrate how improved alignments required changes in machine translation and then present the direct translation model.[…]
