Abstract The neural network joint model of translation (NNJM) is a language model that considers both source and target context to produce a powerful feature for statistical machine translation.However, its softmax top layer necessitates a[…]
Abstract Earlier this year, Chris Manning published an essay observing that “2015 was the year NLP felt the full force of the (deep learning) tsunami.” While expressing excitement for the success of these methods and[…]
Join us at the Don P. Giddens Inaugural Professorial Lecture recognizing Jason Eisner as a full professor in the Department of Computer Science. This lecture will be held from 3:30 to 5 p.m. on Monday,[…]
Abstract Question Answering (QA) is one of the most exciting areas in artificial intelligence today, aiming at augmenting human’s cognitive capabilities with computing systems that are capable of generating meaningful and insightful answers to questions[…]
Abstract The written arguments of students are educational data that can be automatically mined for purposes of student instruction and assessment. This talk will illustrate some of the opportunities and challenges in educationally-oriented argument mining[…]
Abstract We address the problem of acoustic source separation in a deep learning framework we call “deep clustering.” Deep learning has recently produced major improvements in speech enhancement tasks in which the speech and interference[…]
Abstract: It is quite easy to believe that the recently proposed approach to machine translation, called neural machine translation, is simply yet another approach to statistical machine translation. This belief may drive research effort toward[…]
