The BURCHAK corpus: a Challenge Data Set for Interactive Learning of Visually Grounded Word Meanings

We motivate and describe a new freely available human-human dialogue dataset for interactive learning of visually grounded word meanings through ostensive definition by a tutor to a learner. The data has been collected using a novel, character-by-character variant of the DiET chat tool (Healey et al., 2003; Mills and Healey, submitted) with a novel task, where a Learner needs to learn invented visual attribute words (such as " burchak " for square) from a tutor. As such, the text-based interactions closely resemble face-to-face conversation and thus contain many of the linguistic phenomena encountered in natural, spontaneous dialogue. These include self-and other-correction, mid-sentence continuations, interruptions, overlaps, fillers, and hedges. We also present a generic n-gram framework for building user (i.e. tutor) simulations from this type of incremental data, which is freely available to researchers. We show that the simulations produce outputs that are similar to the original data (e.g. 78% turn match similarity). Finally, we train and evaluate a Reinforcement Learning dialogue control agent for learning visually grounded word meanings, trained from the BURCHAK corpus. The learned policy shows comparable performance to a rule-based system built previously.Comment: 10 pages, THE 6TH WORKSHOP ON VISION AND LANGUAGE (VL'17

Towards a personal best : a case for introducing ipsative assessment in higher education

The central role that assessment plays is recognised in higher education, in particular how formative feedback guides learning. A model for effective feedback practice is used to argue that, in current schemes, formative feedback is often not usable because it is strongly linked to external criteria and standards, rather than to the processes of learning. By contrast, ipsative feedback, which is based on a comparison with the learner's previous performance and linked to longterm progress, is likely to be usable and may have additional motivational effects. After recommending a move towards ipsative formative assessment, a further step would be ipsative grading. However, such a radical shift towards a fully ipsative regime might pose new problems and these are discussed. The article explores a compromise of a combined assessment regime. The rewards for learners are potentially high, and the article concludes that ipsative assessment is well worth further investigation. © 2011 Society for Research into Higher Education

Emergent Innovation—a Socio-Epistemological Innovation Technology. Creating Profound Change and Radically New Knowledge as Core Challenges in Knowledge Management

This paper introduces an alternative approach to innovation: Emergent Innovation. As opposed to radical innovation Emergent Innovation finds a balance and integrates the demand both for radically new knowledge and at the same time for an organic development from within the organization. From a knowledge management perspective one can boil down this problem to the question of how to cope with the new and with profound change in knowledge. This question will be dealt with in the first part of the paper. As an implication the alternative approach of Emergent Innovation will be presented in the second part: this approach looks at innovation as a socio-epistemological process of “learning from the future”.\ud Keywords:\ud Innovation, radical innovation, emergent innovation, knowledge creation, change

Learning how to learn: an adaptive dialogue agent for incrementally learning visually grounded word meanings

We present an optimised multi-modal dialogue agent for interactive learning of visually grounded word meanings from a human tutor, trained on real human-human tutoring data. Within a life-long interactive learning period, the agent, trained using Reinforcement Learning (RL), must be able to handle natural conversations with human users and achieve good learning performance (accuracy) while minimising human effort in the learning process. We train and evaluate this system in interaction with a simulated human tutor, which is built on the BURCHAK corpus -- a Human-Human Dialogue dataset for the visual learning task. The results show that: 1) The learned policy can coherently interact with the simulated user to achieve the goal of the task (i.e. learning visual attributes of objects, e.g. colour and shape); and 2) it finds a better trade-off between classifier accuracy and tutoring costs than hand-crafted rule-based policies, including ones with dynamic policies.Comment: 10 pages, RoboNLP Workshop from ACL Conferenc

Modelling Users, Intentions, and Structure in Spoken Dialog

We outline how utterances in dialogs can be interpreted using a partial first order logic. We exploit the capability of this logic to talk about the truth status of formulae to define a notion of coherence between utterances and explain how this coherence relation can serve for the construction of AND/OR trees that represent the segmentation of the dialog. In a BDI model we formalize basic assumptions about dialog and cooperative behaviour of participants. These assumptions provide a basis for inferring speech acts from coherence relations between utterances and attitudes of dialog participants. Speech acts prove to be useful for determining dialog segments defined on the notion of completing expectations of dialog participants. Finally, we sketch how explicit segmentation signalled by cue phrases and performatives is covered by our dialog model.Comment: 17 page