Paul Magron on Wednesday, October 17
Antoine Deleforge on Wednesday, November 14
Date: Wednesday, 14th November 2018 at 2pm
Place: LORIA, room C005
Speaker: Antoine Deleforge (Inria Nancy – Grand Est)
Title: Audio signal processing with a little help from echoes
When a sound wave propagates from a point source through a medium and is reflected on surfaces before reaching microphones, the measured signals consist of mixtures of the direct path signal with delayed and attenuated copies of itself. This acoustical phenomenon is referred to as echoes, or reverberation, and is generally considered as a nuisance in audio signal processing. After introducing some basic signal processing and acoustic background, this seminar will present recent works showing how acoustic echoes can be blindly estimated from audio recordings, and how the knowledge of such echoes can actually help some audio signal processing tasks such as beamforming, source separation or sound source localization.
Emmanuel Dupoux on Wednesday, November 21
Date: Wednesday, 21st November 2018 at 2pm
Place: LORIA, room A008
Speaker: Emmanuel Dupoux (EHESS, Laboratoire de Sciences Cognitives et Psycholinguistique)
Title: Towards developmental AI
Even though current machine learning techniques yield systems that achieve parity with humans on several high level tasks, the learning algorithms themselves are orders of magnitude less data efficient than those used by humans, as evidenced by the speed and resilience with which infants learn language and common sense. I review some of our recent attempts to reverse engineer such abilities in the area of unsupervised or weakly supervised learning of speech representations and speech terms, and the learning the laws of intuitive physics by observation of videos. I argue that a triple effort in data collection, algorithm development and fine grained human/machine comparisons is needed to uncover these developmental algorithms.
Chloé Braud on Wednesday, December 5
Date: Wednesday, 5th December 2018 at 2pm
Place: LORIA, room A008
Speaker: Chloé Braud (CNRS – LORIA)
Title: Transfer learning for discourse parsing
Discourse structures describe the organization of documents in terms of discourse or rhetorical relations (such as « Explanation » or « Contrast ») linking clauses and sentences. Discourse analysis could be useful for various downstream applications, such as automatic summarization, question-answering or sentiment analysis. However, the range of applications and the performance are still limited by the low scores of the existing discourse parsers and their focus on English. Discourse parsing is known to be a hard task: It involves several complex and interacting factors, touching upon all layers of linguistic analysis, from syntax, semantics up to pragmatics. Consequently, also annotation is complex and time consuming, and hence available annotated corpora are sparse and limited in size. In this presentation, I will present attempts to tackle these issues using transfer learning strategies. First, I will describe experiments on identifying implicit discourse relations (i.e. lacking a discourse connective such as « but » or « because ») by transferring knowledge from the explicit examples to the implicit ones, either by augmenting the size of the training set, or by building a task-tailored representation of the words. I will then present two full discourse parsers. The first one involves a combination of several corpora annotated for different languages, leading to improvements on English and to the first systems for Basque and Dutch developed without any training data. The second parser relies on multi-task learning to transfer information among several discourse related tasks.
Philippe Muller on Friday, December 7
Date: Friday, 7th December 2018 at 10am
Place: LORIA, room A008
Speaker: Philippe Muller (IRIT, Toulouse)
Title: Sentential distributional semantics: Learning semantic sentence representations and their compositions (Joint work with Damien Sileo et Tim van de Cruys)
Distributional semantics aims at automatic representation of textual semantic content based on the observation of a large representative corpus. There is a large body of work on lexical distributional semantics, based on the assumption that words appearing in similar contexts should have similar semantic representations. This popularized the representation of words as vectors in a semantic space. More recently, a lot of effort in the NLP field has been devoted to building similar representations for sentences, or even larger textual elements. This raises several questions: how to build sentence representations from word representations in vector spaces, preferably in a compositional manner, and how to guide the representations so that they capture important semantic aspect at the sentence level? Arguably, sequential compositional models such as recurrent neural network offer a simple composition at the lexical level that can be used in supervised settings to make accurate predictions in textual classification, while building a representation of the sentential context in their internal state. This is however specific to each task, and researchers have tried to find ways of building so-called « universal » sentence representations, or more exactly transferable representations. In this perspective several settings have been proposed that evokes supervised distributional approaches at the word level, with auxilliary tasks that could induce semantically relevant representations at the sentence level: for instance trying to predict if two sentences follow each other in a text, or if one is a consequence of the other. These in turn must compose the two sentences in a way that allows for the learning of their relationships. Composition of representations is also important in all tasks that involve predicting a relation between a pair of textual elements: sentence similarity, entailment, discourse relations. The compositions considered in NLP are often quite superficial, and we will show more expressive compositions by taking inspiration from Statistical Relational Learning. Moreover we propose an unsupervised training task to induce sentence representations, based on the prediction of discourse connections between sentences in a large corpus.