Luc De Raedt

Also published as: Luc de Raedt


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Mapping probability word problems to executable representations
Simon Suster | Pieter Fivez | Pietro Totis | Angelika Kimmig | Jesse Davis | Luc de Raedt | Walter Daelemans
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

While solving math word problems automatically has received considerable attention in the NLP community, few works have addressed probability word problems specifically. In this paper, we employ and analyse various neural models for answering such word problems. In a two-step approach, the problem text is first mapped to a formal representation in a declarative language using a sequence-to-sequence model, and then the resulting representation is executed using a probabilistic programming system to provide the answer. Our best performing model incorporates general-domain contextualised word representations that were finetuned using transfer learning on another in-domain dataset. We also apply end-to-end models to this task, which bring out the importance of the two-step approach in obtaining correct solutions to probability problems.


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Learning from Implicit Information in Natural Language Instructions for Robotic Manipulations
Ozan Arkan Can | Pedro Zuidberg Dos Martires | Andreas Persson | Julian Gaal | Amy Loutfi | Luc De Raedt | Deniz Yuret | Alessandro Saffiotti
Proceedings of the Combined Workshop on Spatial Language Understanding (SpLU) and Grounded Communication for Robotics (RoboNLP)

Human-robot interaction often occurs in the form of instructions given from a human to a robot. For a robot to successfully follow instructions, a common representation of the world and objects in it should be shared between humans and the robot so that the instructions can be grounded. Achieving this representation can be done via learning, where both the world representation and the language grounding are learned simultaneously. However, in robotics this can be a difficult task due to the cost and scarcity of data. In this paper, we tackle the problem by separately learning the world representation of the robot and the language grounding. While this approach can address the challenges in getting sufficient data, it may give rise to inconsistencies between both learned components. Therefore, we further propose Bayesian learning to resolve such inconsistencies between the natural language grounding and a robot’s world representation by exploiting spatio-relational information that is implicitly present in instructions given by a human. Moreover, we demonstrate the feasibility of our approach on a scenario involving a robotic arm in the physical world.


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kLogNLP: Graph Kernel–based Relational Learning of Natural Language
Mathias Verbeke | Paolo Frasconi | Kurt De Grave | Fabrizio Costa | Luc De Raedt
Proceedings of 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations


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A Statistical Relational Learning Approach to Identifying Evidence Based Medicine Categories
Mathias Verbeke | Vincent Van Asch | Roser Morante | Paolo Frasconi | Walter Daelemans | Luc De Raedt
Proceedings of the 2012 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning