Tutorial: Human Language Technology and Machine Learning
Date: Wednesday April 27th from 12:00 to 13:00
Place: UPC Campus Nord Building D3. Multimedia Room (entrance by Plaza Telecos)
Abstract: The last 40 years have seen a dramatic progress in machine learning for recognizing speech signals and for translating spoken and written language. This talks will present a unifying view of the underlying statistical methods including the recent developments in deep learning and artificial neural networks. In particular, the talk will address the remarkable fact that, for these tasks and similar tasks like handwriting recognition, the statistical approach makes use of the same four principles: 1) Bayes decision rule for minimum error rate; 2) probabilistic models like hidden Markov models and artificial neural networks; 3) training criteria and algorithms for estimating the free model parameters from large amounts of data; 4) the generation or search process that generates the recognition or translation result. Most of these methods had originally been designed for speech recognition. However, it has turned out that, with suitable modifications, the same concepts carry over to language translation and other tasks in natural language processing.