Date: Friday October 14 at 1 pm
Place: LORIA, room B011-B013
Speaker: Joël Legrand (LIDIAP/EPFL, Lausanne, Switzerland and LORIA, Nancy, France)
Title: Word Sequence Modeling using Deep Learning
Abstract: For a long time, natural language processing (NLP) has relied on generative models with task specific and manually engineered features. Recently, the rapidly growing interest for deep learning has led to state-of-the-art results in various fields such as computer vision, speech processing and natural language processing. The central idea behind these approaches is to learn features and models simultaneously, in an end-to-end manner,
and making as few assumptions as possible. In NLP, word embeddings, mapping words in a dictionary on a continuous low-dimensional vector space, have proven to be very efficient for a large variety of tasks while requiring almost no a-priori linguistic assumptions. In this talk, I will present the results of my research on continuous representations of segments of sentences for the purpose of solving NLP tasks that involve complex sentence-level relationships. I will first introduce the key concepts of deep learning for NLP. I will then focus on two recent empirical studies concerning the tasks of syntactic parsing and bilingual word alignment. For each of them, I will present the main challenges as well as the deep learning-based solutions used to overcome them.
. Joint RNN-based greedy parsing and word composition.
Joël Legrand and Ronan Collobert. Proceeding of the 3rd International Conference on Learning Representations (ICLR 2015)
. Neural Network-based Word Alignment through Score Aggregation.
Joël Legrand and Michael Auli and Ronan Collobert. Proceedings of the First Conference on Machine Translation (ACMT 2016)
Joël Legrand received his MSc degree in Computer Science from the Université de Lorraine and his Ph.D in Electrical Engineering from the École Polytechnique Fédérale de Lausanne. He recently joined the ORPAILLEUR team as a postdoctoral fellow, working on the PractiKPharma project.
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