Training an adaptive dialogue policy for interactive learning of visually grounded word meanings

We present a multi-modal dialogue system for interactive learning of perceptually grounded word meanings from a human tutor. The system integrates an incremental, semantic parsing/generation framework - Dynamic Syntax and Type Theory with Records (DS-TTR) - with a set of visual classifiers that are learned throughout the interaction and which ground the meaning representations that it produces. We use this system in interaction with a simulated human tutor to study the effects of different dialogue policies and capabilities on the accuracy of learned meanings, learning rates, and efforts/costs to the tutor. We show that the overall performance of the learning agent is affected by (1) who takes initiative in the dialogues; (2) the ability to express/use their confidence level about visual attributes; and (3) the ability to process elliptical and incrementally constructed dialogue turns. Ultimately, we train an adaptive dialogue policy which optimises the trade-off between classifier accuracy and tutoring costs.Comment: 11 pages, SIGDIAL 2016 Conferenc

About the nature of Kansei information, from abstract to concrete

Designer’s expertise refers to the scientific fields of emotional design and kansei information. This paper aims to answer to a scientific major issue which is, how to formalize designer’s knowledge, rules, skills into kansei information systems. Kansei can be considered as a psycho-physiologic, perceptive, cognitive and affective process through a particular experience. Kansei oriented methods include various approaches which deal with semantics and emotions, and show the correlation with some design properties. Kansei words may include semantic, sensory, emotional descriptors, and also objects names and product attributes. Kansei levels of information can be seen on an axis going from abstract to concrete dimensions. Sociological value is the most abstract information positioned on this axis. Previous studies demonstrate the values the people aspire to drive their emotional reactions in front of particular semantics. This means that the value dimension should be considered in kansei studies. Through a chain of value-function-product attributes it is possible to enrich design generation and design evaluation processes. This paper describes some knowledge structures and formalisms we established according to this chain, which can be further used for implementing computer aided design tools dedicated to early design. These structures open to new formalisms which enable to integrate design information in a non-hierarchical way. The foreseen algorithmic implementation may be based on the association of ontologies and bag-of-words.AN

A multifrequency study of giant radio sources I. Low-frequency Giant Metrewave Radio Telescope observations of selected sources

We present low-frequency observations with the Giant Metrewave Radio Telescope (GMRT) of a sample of giant radio sources (GRSs), and high-frequency observations of three of these sources with the Very Large Array (VLA). From multifrequency observations of the lobes we estimate the magnetic field strengths using three different approaches, and show that these differ at most by a factor of $\sim$3. For these large radio sources the inverse-Compton losses usually dominate over synchrotron losses when estimates of the classical minimum energy magnetic field are used, consistent with earlier studies. However, this is often not true if the magnetic fields are close to the values estimated using the formalism of Beck & Krause. We also examine the spectral indices of the cores and any evidence of recurrent activity in these sources. We probe the environment using the symmetry parameters of these sources and suggest that their environments are often asymmetric on scales of $\sim$1 Mpc, consistent with earlier studies.Comment: 14 pages, 5 figures, 6 tables, one appendix; accepted for publication in MNRA

Do You See What I Mean? Visual Resolution of Linguistic Ambiguities

Understanding language goes hand in hand with the ability to integrate complex contextual information obtained via perception. In this work, we present a novel task for grounded language understanding: disambiguating a sentence given a visual scene which depicts one of the possible interpretations of that sentence. To this end, we introduce a new multimodal corpus containing ambiguous sentences, representing a wide range of syntactic, semantic and discourse ambiguities, coupled with videos that visualize the different interpretations for each sentence. We address this task by extending a vision model which determines if a sentence is depicted by a video. We demonstrate how such a model can be adjusted to recognize different interpretations of the same underlying sentence, allowing to disambiguate sentences in a unified fashion across the different ambiguity types.Comment: EMNLP 201

Classification of Urban Scenes from Geo-referenced Images in Urban Street-View Context

International audienceThis paper addresses the challenging problem of scene classification in street-view georeferenced images of urban environments. More precisely, the goal of this task is semantic image classification, consisting in predicting in a given image, the presence or absence of a pre-defined class (e.g. shops, vegetation, etc.). The approach is based on the BOSSA representation, which enriches the Bag of Words (BoW) model, in conjunction with the Spatial Pyramid Matching scheme and kernel-based machine learning techniques. The proposed method handles problems that arise in large scale urban environments due to acquisition conditions (static and dynamic objects/pedestrians) combined with the continuous acquisition of data along the vehicle's direction, the varying light conditions and strong occlusions (due to the presence of trees, traffic signs, cars, etc.) giving rise to high intra-class variability. Experiments were conducted on a large dataset of high resolution images collected from two main avenues from the 12th district in Paris and the approach shows promising results