Next seminars

October 14, 2016

  • Joel Legrand (LIDIAP/EPFL, Lausanne, Switzerland and LORIA, Nancy, France)

October 19, 2016

November 2, 2016

November 30, 2016

January 11, 2017

January 18, 2017

February 8, 2017

April 26, 2017

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Joël Legrand

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.

Recent publications:

[1]. Joint RNN-based greedy parsing and word composition.
Joël Legrand and Ronan Collobert. Proceeding of the 3rd International Conference on Learning Representations (ICLR 2015)

[2]. 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)

Short bio:
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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Hans van Ditmarsch

Date: Wednesday, October 19 at 2 pm
Place: LORIA, room C005
Speaker: Hans van Ditmarsch (LORIA, Cello team)

Title: Epistemic Gossip Protocols

A well-studied phenomenon in network theory since the 1970s are optimal schedules to distribute information by one-to-one communication between nodes. One can take these communicative actions to be telephone calls, and protocols to spread information this way are known as gossip protocols or epidemic protocols. Statistical approaches to gossip have taken a large flight since then, witness for example the survey « Epidemic Information Dissemination in Distributed Systems » by Eugster et al. (IEEE Computer, 2004). It is typical to assume a global scheduler who executes a possibly non-deterministic or randomized protocol. A departure from this methodology is to investigate epistemic gossip protocols, where an agent (node) will call another agent not because it is so instructed by a scheduler, but based on its knowledge or ignorance of the distribution of secrets over the network and of other agents’ knowledge or ignorance of that. Such protocols are distributed and do not need a central scheduler. This comes at a cost: they may take longer to terminate than non-epistemic, globally scheduled, protocols. A number of works have appeared over the past years (Apt et al., Attamah et al., van Ditmarsch et al., van Eijck & Gattinger, Herzig & Maffre) of which we present a survey, including open problems yet to be solved by the community.

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Romain Serizel

Date: Wednesday, 2nd November 2016 at 2pm
Place: LORIA, room A008
Speaker: Romain Serizel (LORIA)

Title: Feature learning based on nonnegative matrix factorisation for speaker identification

Abstract: The main target of speaker identification is to assert whether or not the speaker in an audio recording is known and if he/she is known, to find his/her identity. A recent trend is to use feature learning based approaches to overcome the limitations of hand-craft features. This talk will review the dominant paradigm (the so-called I-vector approach) and will propose an alternative solution based on group nonnegative matrix factorisation (NMF). We will then propose to integrate this approach into a task-driven supervised framework that is inspired by supervised dictionary learning. The goal is to capture both the speaker variability and the session variability while exploiting the discriminative learning aspect of the task-driven approach.

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Denis Paperno

Date: Wednesday, November 30 at 2 pm
Place: LORIA, room TBA
Speaker: Denis Paperno (LORIA, Synalp team)

Title: Distributional Semantic Spaces: Creation and Applications

Distributional semantic vectors (also known as word embeddings) are increasingly popular in various natural language tasks. The talk will describe how distributional semantic models are created, investigate some of the model hyperparameters, and illustrate their applications.

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Andreas Vlachos

Date: Wednesday, January 11 at 2 pm
Place: LORIA, room TBA
Speaker: Andreas Vlachos (University of Sheffield, UK)

Title: Imitation learning for structure prediction in natural language processing

Abstract: Imitation learning is a learning paradigm originally developed to learn robotic controllers from demonstrations by humans, e.g. autonomous helicopters from pilot’s demonstrations. Recently, algorithms for structure prediction were proposed under this paradigm and have been applied successfully to a number of tasks such as dependency parsing, information extraction, coreference resolution and semantic parsing. Key advantages are the ability to handle large output search spaces and to learn with non-decomposable loss functions. In this talk I will give a detailed overview of imitation leaning and some recent applications, including its use in training recurrent neural networks.

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Martin Heckmann

Date: Wednesday, 26nd April 2017 at 2pm
Place: LORIA, room A008
Speaker: Martin Heckmann (Honda Research Institute Europe)

Title: Personalized speech interfaces

Abstract: In this presentation I will highlight recent results obtained at the Honda Research Institute Europe GmbH in the context of personalization of speech-based human-machine interfaces. I will first talk about the detection of word prominence. Thereby, I will discuss the performance of prominence detection from noisy audio signals, the contribution of additional visual information on the speaker’s face and head movements as well as different strategies to fuse the two modalities. After that I will present a method to adapt the prominence detection to an individual speaker. The method is inspired by fMLLR, a well-known method in GMM/HMM-based speech recognition systems, and adapted to the SVM-based prominence detection. Next, I will talk about an advanced driver assistance systems (ADAS) which we currently develop to support the driver in inner-city driving and which is controlled via speech. This system will allow the driver to flexibly formulate his requests for assistance while the situation develops. In particular, when facing a left turn at an intersection the driver can delegate the task of observing the right side traffic to the system as he would do to a co-driver. The system will then inform him when there is an appropriate gap in the traffic to make the turn. Results of a user study we performed show that drivers largely prefer our proposed system to an alternative visual system or driving without any assistance. In this context I will show results on the estimation of the individual driver’s left turning behavior. Based on these driver models the interaction with the driver can be personalized to further improve the usefulness of the system.

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