LTI Colloquium Fall 2012
Course Information
The LTI colloquium is a series of talks related to language technologies. The topics include but are not restricted to Computational Linguistics, Machine Translation, Speech Recognition and Synthesis, Information Retrieval, Computational Biology, Machine Learning, Text Mining, Knowledge Representation, Computer-Assisted Language Learning and Intelligent Language Tutoring. To get credit for the course, students are required to write either a short critique of one of the presentations or a comparison of two.
| Time: | Fridays 2:30-4:00pm |
| Location: | Doherty Hall 2315 |
| Instructor: | Roni Rosenfeld, roni (at) cs.cmu.edu |
| TA: | Reyyan Yeniterzi, reyyan (at) cs.cmu.edu |
| Course Secretary: | Kate Schaich, kschaich (at) cs.cmu.edu |
Upcoming Talk
December 7, Friday, 2:30pm
Doherty Hall 2315
Anoop Sarkar
Simon Fraser University
Ensemble Decoding for Statistical Machine Translation
Statistical machine translation is often faced with the problem of combining data from many diverse sources into a single translation model. In this talk we introduce a novel approach called ensemble decoding that combines multiple translation models during the process of translation. We show that this technique is applicable in many diverse areas in machine translation:
(a) Domain adaptation is needed when the training data is from a different domain than the test data. We show that ensemble decoding can effectively combine out-of-domain and in-domain translation models.
(b) Multi-metric optimization modifies discriminative training for machine translation to prefer Pareto-optimal points with respect to multiple evaluation measures. We use ensemble decoding to combine the Pareto-optimal weight vectors obtained in multi-metric optimization. Furthermore, the ensemble weights are tuned to prefer Pareto-optimal solutions.
(c) In translation out of resource-poor languages, a pivot language is often used to augment the translation model from source to target. Ensemble models provide a novel way to combine the direct translation model (from source to target) and the pivot model (from source to pivot to target).
Bio: Anoop Sarkar is an Associate Professor at Simon Fraser University in British Columbia, Canada where he co-directs the Natural Language Laboratory (http://natlang.cs.sfu.ca). He received his Ph.D. from the Department of Computer and Information Sciences at the University of Pennsylvania under Prof. Aravind Joshi for his work on semi-supervised statistical parsing using tree-adjoining grammars.
His research is focused on statistical parsing and machine translation: in the areas of syntax and morphology in MT, semi-supervised learning, and domain adaptation. His interests also include formal language theory and stochastic grammars, in particular tree automata and tree-adjoining grammars.
Schedule
Past Colloquia