|Australasian Language Technology Workshop 2014|
Tutorials at ALTA 2014
Presenter: Dr. Trevor Cohn and Daniel Beck
Title: Gaussian Processes for NLP
This tutorial aims to cover the basic motivation, ideas and theory of Gaussian Processes and several applications to natural language processing tasks. Gaussian Processes (GPs) are a powerful modelling framework incorporating kernels and Bayesian inference, and are recognised as state-of-the-art for many machine learning tasks. This tutorial will focus primarily on regression and classification, both fundamental techniques of wide-spread use in the NLP community. We argue that the GP framework offers many benefits over commonly used machine learning frameworks, such as linear models (logistic regression, least squares regression) and support vector machines (SVMs). GPs have the advantage of being a fully Bayesian model, giving a posterior over the desired variables. Their probabilistic formulation allows for much wider applicability in larger graphical models, unlike SVMs. Moreover, several properties of Gaussian distributions means that GP (regression) supports analytic formulations for the posterior and predictive inference, avoiding the many approximation errors that plague approximate inference techniques in common use for Bayesian models (e.g. MCMCM, variational Bayes). GPs provide an elegant, flexible and simple means of probabilistic inference. GPs have been actively researched since the early 2000s, and are now reaching maturity: the fundamental theory and practice is well understood, and now research is focused into their applications, and improve inference algorithms, e.g. for scaling inference to large and high-dimensional datasets. Several open-source packages (e.g. GPy and GPML) have been developed which allow for GPs to be easily used for many applications. This tutorial aims to present the main ideas and theory behind GPs and recent applications to NLP, emphasising their potential for widespread application across many NLP tasks.
For more information, see Trevor's page for the tutorial.
Unfortunately, due to unforeseen circumstances, this tutorial has been cancelled.
Presenter: Dr. Gholamreza Haffari
Title: Machine Learning Approaches for Dealing with Limited Bilingual Data in Statistical Machine Translation
Short Description:High quality translation output in Statistical machine translation (SMT) is dependent on the availability of massive amounts of parallel text in the source and target language. There are a large number of languages that are considered "low-density", either because the population speaking the language is not very large, or even if millions of people speak the language, insufficient online resources are available in that language. This tutorial covers machine learning approaches for dealing with such situations in SMT where the amount of available bilingual data is limited.