Johan Bos

Date: Wednesday, December 7 at 2:15pm
Place: LORIA, Amphi C
Speaker: Johan Bos (Rijksuniversiteit Groningen)

Title: The Parallel Meaning Bank: A Large Corpus of Translated Texts Annotated with Formal Meaning Representations

Abstract:
Several large corpora annotated with meaning representations are nowadays available such as the Groningen Meaning Bank, the AMR Corpus, or Treebank Semantics. These are usually resources for a single language. In this paper I present a project with the aim to develop a meaning bank for translations of texts — in other words, a parallel meaning bank. The languages involved are English, Dutch, German and Italian. The idea is to use language technology developed for English and project the outcome of the analyses to the other languages. There are five steps of processing:
– Tokenisation: segmentation of words, multi-word expressions and sentences, using Elephant, a statistical tokenizer;
– Semantic Tagging: mapping word tokens to semantic tags (abstracting over traditional part-of-speech tags and named entities and a bit more);
– Symbolisation: assigning appropriate non-logical symbols to word tokens (combining lemmatization and normalisation);
– Syntactic Parsing: based on Combinatorial Categorial Grammar;
– Semantic Parsing: based on Discourse Representation Theory, using the semantic parser Boxer;
The first aim of the project is to provide appropriate compositional semantic analyses for the aforementioned language taking advantage of the translations. The second aim is to study the role of meaning in translations: even though you would expect that meaning is preserved in translations, human translators often perform little tricks involving meaning shifts and changes to arrive at better translations.

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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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Marie-Francine Moens

Date: Wednesday, March 8 at 2 pm
Place: LORIA, room TBA
Speaker: Marie-Francine Moens (KU Leuven)

Title: Acquiring Knowledge from Multimodal Sources to Aid Language Understanding

Abstract: Human language understanding (HLU) by a machine is of large economic and social value. In this lecture we consider language understanding of written text. First, we give an overview of the latest methods for HLU that map language to a formal knowledge representation which facilitates other automated tasks. Most current HLU systems are trained on texts that are manually annotated, which are often lacking in open domain applications. In addition, much content is left implicit in a text, which when humans read a text is inferred by relying on their world and common sense knowledge. We go deeper into the field of representation learning that nowadays is very much studied in computational linguistics. This field investigates methods for representing language as statistical concepts or as vectors, allowing straightforward methods of compositionality. The methods often use deep learning and its underlying neural network technologies to learn concepts from large text collections in an unsupervised way (i.e., without the need for manual annotations). We show how these methods can help, but also demonstrate that these methods are still insufficient to automatically acquire the necessary background knowledge and more specifically world and common sense knowledge needed for language understanding. We go deeper in on how we can learn knowledge jointly from textual and visual data to help language understanding, which will be illustrated with the first results obtained in the MUSTER CHIST-ERA project.

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Martin Heckmann

Date: Wednesday, 29th March 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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